-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/


-- | Amazon Machine Learning SDK.
--   
--   The types from this library are intended to be used with
--   <a>amazonka</a>, which provides mechanisms for specifying AuthN/AuthZ
--   information, sending requests, and receiving responses.
--   
--   Lenses are used for constructing and manipulating types, due to the
--   depth of nesting of AWS types and transparency regarding
--   de/serialisation into more palatable Haskell values. The provided
--   lenses should be compatible with any of the major lens libraries such
--   as <a>lens</a> or <a>lens-family-core</a>.
--   
--   See <a>Network.AWS.MachineLearning</a> or <a>the AWS documentation</a>
--   to get started.
@package amazonka-ml
@version 1.6.0


module Network.AWS.MachineLearning.Types

-- | API version <tt>2014-12-12</tt> of the Amazon Machine Learning SDK
--   configuration.
machineLearning :: Service

-- | Prism for InvalidTagException' errors.
_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the server occurred when trying to process a request.
_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the client occurred. Typically, the cause is an invalid
--   input value.
_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A second request to use or change an object was not allowed. This can
--   result from retrying a request using a parameter that was not present
--   in the original request.
_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError

-- | Prism for TagLimitExceededException' errors.
_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The exception is thrown when a predict request is made to an unmounted
--   <tt>MLModel</tt> .
_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A specified resource cannot be located.
_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The subscriber exceeded the maximum number of operations. This
--   exception can occur when listing objects such as <tt>DataSource</tt> .
_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The function used to train an <tt>MLModel</tt> . Training choices
--   supported by Amazon ML include the following:
--   
--   <ul>
--   <li><tt>SGD</tt> - Stochastic Gradient Descent. *
--   <tt>RandomForest</tt> - Random forest of decision trees.</li>
--   </ul>
data Algorithm
SGD :: Algorithm

-- | A list of the variables to use in searching or filtering
--   <tt>BatchPrediction</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>BatchPrediction</tt> creation date. * <tt>Status</tt> - Sets the
--   search criteria to <tt>BatchPrediction</tt> status. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>BatchPrediction</tt>
--   ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage Service
--   (Amazon S3) bucket or directory.</li>
--   </ul>
data BatchPredictionFilterVariable
BatchCreatedAt :: BatchPredictionFilterVariable
BatchDataSourceId :: BatchPredictionFilterVariable
BatchDataURI :: BatchPredictionFilterVariable
BatchIAMUser :: BatchPredictionFilterVariable
BatchLastUpdatedAt :: BatchPredictionFilterVariable
BatchMLModelId :: BatchPredictionFilterVariable
BatchName :: BatchPredictionFilterVariable
BatchStatus :: BatchPredictionFilterVariable

-- | A list of the variables to use in searching or filtering
--   <tt>DataSource</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>DataSource</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>DataSource</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>DataSource</tt> ____
--   <tt>Name</tt> . * <tt>DataUri</tt> - Sets the search criteria to the
--   URI of data files used to create the <tt>DataSource</tt> . The URI can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory. * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>DataSource</tt> creation.</li>
--   </ul>
data DataSourceFilterVariable
DataCreatedAt :: DataSourceFilterVariable
DataDATALOCATIONS3 :: DataSourceFilterVariable
DataIAMUser :: DataSourceFilterVariable
DataLastUpdatedAt :: DataSourceFilterVariable
DataName :: DataSourceFilterVariable
DataStatus :: DataSourceFilterVariable

-- | Contains the key values of <tt>DetailsMap</tt> :
--   <tt>PredictiveModelType</tt> - Indicates the type of the
--   <tt>MLModel</tt> . <tt>Algorithm</tt> - Indicates the algorithm that
--   was used for the <tt>MLModel</tt> .
data DetailsAttributes
Algorithm :: DetailsAttributes
PredictiveModelType :: DetailsAttributes

-- | Object status with the following possible values:
--   
--   <ul>
--   <li><tt>PENDING</tt> * <tt>INPROGRESS</tt> * <tt>FAILED</tt> *
--   <tt>COMPLETED</tt> * <tt>DELETED</tt></li>
--   </ul>
data EntityStatus
ESCompleted :: EntityStatus
ESDeleted :: EntityStatus
ESFailed :: EntityStatus
ESInprogress :: EntityStatus
ESPending :: EntityStatus

-- | A list of the variables to use in searching or filtering
--   <tt>Evaluation</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>Evaluation</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>Evaluation</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>Evaluation</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked an evaluation. * <tt>MLModelId</tt> - Sets
--   the search criteria to the <tt>Predictor</tt> that was evaluated. *
--   <tt>DataSourceId</tt> - Sets the search criteria to the
--   <tt>DataSource</tt> used in evaluation. * <tt>DataUri</tt> - Sets the
--   search criteria to the data file(s) used in evaluation. The URL can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory.</li>
--   </ul>
data EvaluationFilterVariable
EvalCreatedAt :: EvaluationFilterVariable
EvalDataSourceId :: EvaluationFilterVariable
EvalDataURI :: EvaluationFilterVariable
EvalIAMUser :: EvaluationFilterVariable
EvalLastUpdatedAt :: EvaluationFilterVariable
EvalMLModelId :: EvaluationFilterVariable
EvalName :: EvaluationFilterVariable
EvalStatus :: EvaluationFilterVariable
data MLModelFilterVariable
MLMFVAlgorithm :: MLModelFilterVariable
MLMFVCreatedAt :: MLModelFilterVariable
MLMFVIAMUser :: MLModelFilterVariable
MLMFVLastUpdatedAt :: MLModelFilterVariable
MLMFVMLModelType :: MLModelFilterVariable
MLMFVName :: MLModelFilterVariable
MLMFVRealtimeEndpointStatus :: MLModelFilterVariable
MLMFVStatus :: MLModelFilterVariable
MLMFVTrainingDataSourceId :: MLModelFilterVariable
MLMFVTrainingDataURI :: MLModelFilterVariable
data MLModelType
Binary :: MLModelType
Multiclass :: MLModelType
Regression :: MLModelType
data RealtimeEndpointStatus
Failed :: RealtimeEndpointStatus
None :: RealtimeEndpointStatus
Ready :: RealtimeEndpointStatus
Updating :: RealtimeEndpointStatus

-- | The sort order specified in a listing condition. Possible values
--   include the following:
--   
--   <ul>
--   <li><tt>asc</tt> - Present the information in ascending order (from
--   A-Z). * <tt>dsc</tt> - Present the information in descending order
--   (from Z-A).</li>
--   </ul>
data SortOrder
Asc :: SortOrder
Dsc :: SortOrder
data TaggableResourceType
BatchPrediction :: TaggableResourceType
DataSource :: TaggableResourceType
Evaluation :: TaggableResourceType
MLModel :: TaggableResourceType

-- | Represents the output of a <tt>GetBatchPrediction</tt> operation.
--   
--   The content consists of the detailed metadata, the status, and the
--   data file information of a <tt>Batch Prediction</tt> .
--   
--   <i>See:</i> <a>batchPrediction</a> smart constructor.
data BatchPrediction

-- | Creates a value of <a>BatchPrediction</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>bpStatus</a> - The status of the <tt>BatchPrediction</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to generate
--   predictions for a batch of observations. * <tt>INPROGRESS</tt> - The
--   process is underway. * <tt>FAILED</tt> - The request to perform a
--   batch prediction did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The batch prediction process completed
--   successfully. * <tt>DELETED</tt> - The <tt>BatchPrediction</tt> is
--   marked as deleted. It is not usable.</li>
--   <li><a>bpLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>bpCreatedAt</a> - The time that the <tt>BatchPrediction</tt>
--   was created. The time is expressed in epoch time.</li>
--   <li><a>bpComputeTime</a> - Undocumented member.</li>
--   <li><a>bpInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>bpMLModelId</a> - The ID of the <tt>MLModel</tt> that generated
--   predictions for the <tt>BatchPrediction</tt> request.</li>
--   <li><a>bpBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that points to the group of observations to
--   predict.</li>
--   <li><a>bpTotalRecordCount</a> - Undocumented member.</li>
--   <li><a>bpStartedAt</a> - Undocumented member.</li>
--   <li><a>bpBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> at creation. This value should be identical
--   to the value of the <tt>BatchPredictionID</tt> in the request.</li>
--   <li><a>bpFinishedAt</a> - Undocumented member.</li>
--   <li><a>bpInvalidRecordCount</a> - Undocumented member.</li>
--   <li><a>bpCreatedByIAMUser</a> - The AWS user account that invoked the
--   <tt>BatchPrediction</tt> . The account type can be either an AWS root
--   account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>bpName</a> - A user-supplied name or description of the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>bpMessage</a> - A description of the most recent details about
--   processing the batch prediction request.</li>
--   <li><a>bpOutputURI</a> - The location of an Amazon S3 bucket or
--   directory to receive the operation results. The following substrings
--   are not allowed in the <tt>s3 key</tt> portion of the
--   <tt>outputURI</tt> field: <tt>:</tt>, <tt>//</tt>, <tt>/./</tt>,
--   <tt>/../</tt>.</li>
--   </ul>
batchPrediction :: BatchPrediction

-- | The status of the <tt>BatchPrediction</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to generate predictions for a batch of
--   observations. * <tt>INPROGRESS</tt> - The process is underway. *
--   <tt>FAILED</tt> - The request to perform a batch prediction did not
--   run to completion. It is not usable. * <tt>COMPLETED</tt> - The batch
--   prediction process completed successfully. * <tt>DELETED</tt> - The
--   <tt>BatchPrediction</tt> is marked as deleted. It is not usable.
bpStatus :: Lens' BatchPrediction (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The time that the <tt>BatchPrediction</tt> was created. The time is
--   expressed in epoch time.
bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpComputeTime :: Lens' BatchPrediction (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that generated predictions for the
--   <tt>BatchPrediction</tt> request.
bpMLModelId :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that points to the group of
--   observations to predict.
bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | Undocumented member.
bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The ID assigned to the <tt>BatchPrediction</tt> at creation. This
--   value should be identical to the value of the
--   <tt>BatchPredictionID</tt> in the request.
bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | The AWS user account that invoked the <tt>BatchPrediction</tt> . The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text)

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
bpName :: Lens' BatchPrediction (Maybe Text)

-- | A description of the most recent details about processing the batch
--   prediction request.
bpMessage :: Lens' BatchPrediction (Maybe Text)

-- | The location of an Amazon S3 bucket or directory to receive the
--   operation results. The following substrings are not allowed in the
--   <tt>s3 key</tt> portion of the <tt>outputURI</tt> field: <tt>:</tt>,
--   <tt>//</tt>, <tt>/./</tt>, <tt>/../</tt>.
bpOutputURI :: Lens' BatchPrediction (Maybe Text)

-- | Represents the output of the <tt>GetDataSource</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>dataSource</a> smart constructor.
data DataSource

-- | Creates a value of <a>DataSource</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dsStatus</a> - The current status of the <tt>DataSource</tt> .
--   This element can have one of the following values: * PENDING - Amazon
--   Machine Learning (Amazon ML) submitted a request to create a
--   <tt>DataSource</tt> . * INPROGRESS - The creation process is underway.
--   * FAILED - The request to create a <tt>DataSource</tt> did not run to
--   completion. It is not usable. * COMPLETED - The creation process
--   completed successfully. * DELETED - The <tt>DataSource</tt> is marked
--   as deleted. It is not usable.</li>
--   <li><a>dsNumberOfFiles</a> - The number of data files referenced by
--   the <tt>DataSource</tt> .</li>
--   <li><a>dsLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>dsCreatedAt</a> - The time that the <tt>DataSource</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>dsComputeTime</a> - Undocumented member.</li>
--   <li><a>dsDataSourceId</a> - The ID that is assigned to the
--   <tt>DataSource</tt> during creation.</li>
--   <li><a>dsRDSMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataSizeInBytes</a> - The total number of observations
--   contained in the data files that the <tt>DataSource</tt>
--   references.</li>
--   <li><a>dsStartedAt</a> - Undocumented member.</li>
--   <li><a>dsFinishedAt</a> - Undocumented member.</li>
--   <li><a>dsCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>DataSource</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>dsName</a> - A user-supplied name or description of the
--   <tt>DataSource</tt> .</li>
--   <li><a>dsDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Service (Amazon S3) that is used by a
--   <tt>DataSource</tt> .</li>
--   <li><a>dsComputeStatistics</a> - The parameter is <tt>true</tt> if
--   statistics need to be generated from the observation data.</li>
--   <li><a>dsMessage</a> - A description of the most recent details about
--   creating the <tt>DataSource</tt> .</li>
--   <li><a>dsRedshiftMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement requirement used when this
--   <tt>DataSource</tt> was created.</li>
--   <li><a>dsRoleARN</a> - Undocumented member.</li>
--   </ul>
dataSource :: DataSource

-- | The current status of the <tt>DataSource</tt> . This element can have
--   one of the following values: * PENDING - Amazon Machine Learning
--   (Amazon ML) submitted a request to create a <tt>DataSource</tt> . *
--   INPROGRESS - The creation process is underway. * FAILED - The request
--   to create a <tt>DataSource</tt> did not run to completion. It is not
--   usable. * COMPLETED - The creation process completed successfully. *
--   DELETED - The <tt>DataSource</tt> is marked as deleted. It is not
--   usable.
dsStatus :: Lens' DataSource (Maybe EntityStatus)

-- | The number of data files referenced by the <tt>DataSource</tt> .
dsNumberOfFiles :: Lens' DataSource (Maybe Integer)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime)

-- | The time that the <tt>DataSource</tt> was created. The time is
--   expressed in epoch time.
dsCreatedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsComputeTime :: Lens' DataSource (Maybe Integer)

-- | The ID that is assigned to the <tt>DataSource</tt> during creation.
dsDataSourceId :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata)

-- | The total number of observations contained in the data files that the
--   <tt>DataSource</tt> references.
dsDataSizeInBytes :: Lens' DataSource (Maybe Integer)

-- | Undocumented member.
dsStartedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsFinishedAt :: Lens' DataSource (Maybe UTCTime)

-- | The AWS user account from which the <tt>DataSource</tt> was created.
--   The account type can be either an AWS root account or an AWS Identity
--   and Access Management (IAM) user account.
dsCreatedByIAMUser :: Lens' DataSource (Maybe Text)

-- | A user-supplied name or description of the <tt>DataSource</tt> .
dsName :: Lens' DataSource (Maybe Text)

-- | The location and name of the data in Amazon Simple Storage Service
--   (Amazon S3) that is used by a <tt>DataSource</tt> .
dsDataLocationS3 :: Lens' DataSource (Maybe Text)

-- | The parameter is <tt>true</tt> if statistics need to be generated from
--   the observation data.
dsComputeStatistics :: Lens' DataSource (Maybe Bool)

-- | A description of the most recent details about creating the
--   <tt>DataSource</tt> .
dsMessage :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)

-- | A JSON string that represents the splitting and rearrangement
--   requirement used when this <tt>DataSource</tt> was created.
dsDataRearrangement :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRoleARN :: Lens' DataSource (Maybe Text)

-- | Represents the output of <tt>GetEvaluation</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>evaluation</a> smart constructor.
data Evaluation

-- | Creates a value of <a>Evaluation</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>eStatus</a> - The status of the evaluation. This element can
--   have one of the following values: * <tt>PENDING</tt> - Amazon Machine
--   Learning (Amazon ML) submitted a request to evaluate an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The evaluation is underway.
--   * <tt>FAILED</tt> - The request to evaluate an <tt>MLModel</tt> did
--   not run to completion. It is not usable. * <tt>COMPLETED</tt> - The
--   evaluation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>Evaluation</tt> is marked as deleted. It is not usable.</li>
--   <li><a>ePerformanceMetrics</a> - Measurements of how well the
--   <tt>MLModel</tt> performed, using observations referenced by the
--   <tt>DataSource</tt> . One of the following metrics is returned, based
--   on the type of the <tt>MLModel</tt> : * BinaryAUC: A binary
--   <tt>MLModel</tt> uses the Area Under the Curve (AUC) technique to
--   measure performance. * RegressionRMSE: A regression <tt>MLModel</tt>
--   uses the Root Mean Square Error (RMSE) technique to measure
--   performance. RMSE measures the difference between predicted and actual
--   values for a single variable. * MulticlassAvgFScore: A multiclass
--   <tt>MLModel</tt> uses the F1 score technique to measure performance.
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .</li>
--   <li><a>eLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>Evaluation</tt> . The time is expressed in epoch time.</li>
--   <li><a>eCreatedAt</a> - The time that the <tt>Evaluation</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>eComputeTime</a> - Undocumented member.</li>
--   <li><a>eInputDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Server (Amazon S3) that is used in the
--   evaluation.</li>
--   <li><a>eMLModelId</a> - The ID of the <tt>MLModel</tt> that is the
--   focus of the evaluation.</li>
--   <li><a>eStartedAt</a> - Undocumented member.</li>
--   <li><a>eFinishedAt</a> - Undocumented member.</li>
--   <li><a>eCreatedByIAMUser</a> - The AWS user account that invoked the
--   evaluation. The account type can be either an AWS root account or an
--   AWS Identity and Access Management (IAM) user account.</li>
--   <li><a>eName</a> - A user-supplied name or description of the
--   <tt>Evaluation</tt> .</li>
--   <li><a>eEvaluationId</a> - The ID that is assigned to the
--   <tt>Evaluation</tt> at creation.</li>
--   <li><a>eMessage</a> - A description of the most recent details about
--   evaluating the <tt>MLModel</tt> .</li>
--   <li><a>eEvaluationDataSourceId</a> - The ID of the <tt>DataSource</tt>
--   that is used to evaluate the <tt>MLModel</tt> .</li>
--   </ul>
evaluation :: Evaluation

-- | The status of the evaluation. This element can have one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Learning (Amazon
--   ML) submitted a request to evaluate an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The evaluation is underway. * <tt>FAILED</tt> -
--   The request to evaluate an <tt>MLModel</tt> did not run to completion.
--   It is not usable. * <tt>COMPLETED</tt> - The evaluation process
--   completed successfully. * <tt>DELETED</tt> - The <tt>Evaluation</tt>
--   is marked as deleted. It is not usable.
eStatus :: Lens' Evaluation (Maybe EntityStatus)

-- | Measurements of how well the <tt>MLModel</tt> performed, using
--   observations referenced by the <tt>DataSource</tt> . One of the
--   following metrics is returned, based on the type of the
--   <tt>MLModel</tt> : * BinaryAUC: A binary <tt>MLModel</tt> uses the
--   Area Under the Curve (AUC) technique to measure performance. *
--   RegressionRMSE: A regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable. * MulticlassAvgFScore: A multiclass <tt>MLModel</tt> uses
--   the F1 score technique to measure performance. For more information
--   about performance metrics, please see the <a>Amazon Machine Learning
--   Developer Guide</a> .
ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)

-- | The time of the most recent edit to the <tt>Evaluation</tt> . The time
--   is expressed in epoch time.
eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The time that the <tt>Evaluation</tt> was created. The time is
--   expressed in epoch time.
eCreatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eComputeTime :: Lens' Evaluation (Maybe Integer)

-- | The location and name of the data in Amazon Simple Storage Server
--   (Amazon S3) that is used in the evaluation.
eInputDataLocationS3 :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that is the focus of the evaluation.
eMLModelId :: Lens' Evaluation (Maybe Text)

-- | Undocumented member.
eStartedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eFinishedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The AWS user account that invoked the evaluation. The account type can
--   be either an AWS root account or an AWS Identity and Access Management
--   (IAM) user account.
eCreatedByIAMUser :: Lens' Evaluation (Maybe Text)

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
eName :: Lens' Evaluation (Maybe Text)

-- | The ID that is assigned to the <tt>Evaluation</tt> at creation.
eEvaluationId :: Lens' Evaluation (Maybe Text)

-- | A description of the most recent details about evaluating the
--   <tt>MLModel</tt> .
eMessage :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that is used to evaluate the
--   <tt>MLModel</tt> .
eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text)

-- | Represents the output of a <tt>GetMLModel</tt> operation.
--   
--   The content consists of the detailed metadata and the current status
--   of the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>mLModel</a> smart constructor.
data MLModel

-- | Creates a value of <a>MLModel</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>mlmStatus</a> - The current status of an <tt>MLModel</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to create an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The creation process is
--   underway. * <tt>FAILED</tt> - The request to create an
--   <tt>MLModel</tt> didn't run to completion. The model isn't usable. *
--   <tt>COMPLETED</tt> - The creation process completed successfully. *
--   <tt>DELETED</tt> - The <tt>MLModel</tt> is marked as deleted. It isn't
--   usable.</li>
--   <li><a>mlmLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>MLModel</tt> . The time is expressed in epoch time.</li>
--   <li><a>mlmTrainingParameters</a> - A list of the training parameters
--   in the <tt>MLModel</tt> . The list is implemented as a map of
--   key-value pairs. The following is the current set of training
--   parameters: * <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed
--   size of the model. Depending on the input data, the size of the model
--   might affect its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>mlmScoreThresholdLastUpdatedAt</a> - The time of the most
--   recent edit to the <tt>ScoreThreshold</tt> . The time is expressed in
--   epoch time.</li>
--   <li><a>mlmCreatedAt</a> - The time that the <tt>MLModel</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>mlmComputeTime</a> - Undocumented member.</li>
--   <li><a>mlmInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>mlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt> at
--   creation.</li>
--   <li><a>mlmSizeInBytes</a> - Undocumented member.</li>
--   <li><a>mlmStartedAt</a> - Undocumented member.</li>
--   <li><a>mlmScoreThreshold</a> - Undocumented member.</li>
--   <li><a>mlmFinishedAt</a> - Undocumented member.</li>
--   <li><a>mlmAlgorithm</a> - The algorithm used to train the
--   <tt>MLModel</tt> . The following algorithm is supported: *
--   <tt>SGD</tt> -- Stochastic gradient descent. The goal of <tt>SGD</tt>
--   is to minimize the gradient of the loss function.</li>
--   <li><a>mlmCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>MLModel</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>mlmName</a> - A user-supplied name or description of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmEndpointInfo</a> - The current endpoint of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmTrainingDataSourceId</a> - The ID of the training
--   <tt>DataSource</tt> . The <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .</li>
--   <li><a>mlmMessage</a> - A description of the most recent details about
--   accessing the <tt>MLModel</tt> .</li>
--   <li><a>mlmMLModelType</a> - Identifies the <tt>MLModel</tt> category.
--   The following are the available types: * <tt>REGRESSION</tt> -
--   Produces a numeric result. For example, "What price should a house be
--   listed at?" * <tt>BINARY</tt> - Produces one of two possible results.
--   For example, "Is this a child-friendly web site?". *
--   <tt>MULTICLASS</tt> - Produces one of several possible results. For
--   example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".</li>
--   </ul>
mLModel :: MLModel

-- | The current status of an <tt>MLModel</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to create an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The creation process is underway. *
--   <tt>FAILED</tt> - The request to create an <tt>MLModel</tt> didn't run
--   to completion. The model isn't usable. * <tt>COMPLETED</tt> - The
--   creation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>MLModel</tt> is marked as deleted. It isn't usable.
mlmStatus :: Lens' MLModel (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>MLModel</tt> . The time is
--   expressed in epoch time.
mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
mlmTrainingParameters :: Lens' MLModel (HashMap Text Text)

-- | The time of the most recent edit to the <tt>ScoreThreshold</tt> . The
--   time is expressed in epoch time.
mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | The time that the <tt>MLModel</tt> was created. The time is expressed
--   in epoch time.
mlmCreatedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmComputeTime :: Lens' MLModel (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text)

-- | The ID assigned to the <tt>MLModel</tt> at creation.
mlmMLModelId :: Lens' MLModel (Maybe Text)

-- | Undocumented member.
mlmSizeInBytes :: Lens' MLModel (Maybe Integer)

-- | Undocumented member.
mlmStartedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmScoreThreshold :: Lens' MLModel (Maybe Double)

-- | Undocumented member.
mlmFinishedAt :: Lens' MLModel (Maybe UTCTime)

-- | The algorithm used to train the <tt>MLModel</tt> . The following
--   algorithm is supported: * <tt>SGD</tt> -- Stochastic gradient descent.
--   The goal of <tt>SGD</tt> is to minimize the gradient of the loss
--   function.
mlmAlgorithm :: Lens' MLModel (Maybe Algorithm)

-- | The AWS user account from which the <tt>MLModel</tt> was created. The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
mlmName :: Lens' MLModel (Maybe Text)

-- | The current endpoint of the <tt>MLModel</tt> .
mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)

-- | The ID of the training <tt>DataSource</tt> . The
--   <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .
mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text)

-- | A description of the most recent details about accessing the
--   <tt>MLModel</tt> .
mlmMessage :: Lens' MLModel (Maybe Text)

-- | Identifies the <tt>MLModel</tt> category. The following are the
--   available types: * <tt>REGRESSION</tt> - Produces a numeric result.
--   For example, "What price should a house be listed at?" *
--   <tt>BINARY</tt> - Produces one of two possible results. For example,
--   "Is this a child-friendly web site?". * <tt>MULTICLASS</tt> - Produces
--   one of several possible results. For example, "Is this a HIGH-, LOW-,
--   or MEDIUM-risk trade?".
mlmMLModelType :: Lens' MLModel (Maybe MLModelType)

-- | Measurements of how well the <tt>MLModel</tt> performed on known
--   observations. One of the following metrics is returned, based on the
--   type of the <tt>MLModel</tt> :
--   
--   <ul>
--   <li>BinaryAUC: The binary <tt>MLModel</tt> uses the Area Under the
--   Curve (AUC) technique to measure performance.</li>
--   <li>RegressionRMSE: The regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable.</li>
--   <li>MulticlassAvgFScore: The multiclass <tt>MLModel</tt> uses the F1
--   score technique to measure performance.</li>
--   </ul>
--   
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .
--   
--   <i>See:</i> <a>performanceMetrics</a> smart constructor.
data PerformanceMetrics

-- | Creates a value of <a>PerformanceMetrics</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pmProperties</a> - Undocumented member.</li>
--   </ul>
performanceMetrics :: PerformanceMetrics

-- | Undocumented member.
pmProperties :: Lens' PerformanceMetrics (HashMap Text Text)

-- | The output from a <tt>Predict</tt> operation:
--   
--   <ul>
--   <li><tt>Details</tt> - Contains the following attributes:
--   <tt>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY |
--   MULTICLASS</tt> <tt>DetailsAttributes.ALGORITHM - SGD</tt></li>
--   <li><tt>PredictedLabel</tt> - Present for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> request.</li>
--   <li><tt>PredictedScores</tt> - Contains the raw classification score
--   corresponding to each label.</li>
--   <li><tt>PredictedValue</tt> - Present for a <tt>REGRESSION</tt>
--   <tt>MLModel</tt> request.</li>
--   </ul>
--   
--   <i>See:</i> <a>prediction</a> smart constructor.
data Prediction

-- | Creates a value of <a>Prediction</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pPredictedValue</a> - The prediction value for
--   <tt>REGRESSION</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedLabel</a> - The prediction label for either a
--   <tt>BINARY</tt> or <tt>MULTICLASS</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedScores</a> - Undocumented member.</li>
--   <li><a>pDetails</a> - Undocumented member.</li>
--   </ul>
prediction :: Prediction

-- | The prediction value for <tt>REGRESSION</tt> <tt>MLModel</tt> .
pPredictedValue :: Lens' Prediction (Maybe Double)

-- | The prediction label for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> .
pPredictedLabel :: Lens' Prediction (Maybe Text)

-- | Undocumented member.
pPredictedScores :: Lens' Prediction (HashMap Text Double)

-- | Undocumented member.
pDetails :: Lens' Prediction (HashMap DetailsAttributes Text)

-- | The data specification of an Amazon Relational Database Service
--   (Amazon RDS) <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>rdsDataSpec</a> smart constructor.
data RDSDataSpec

-- | Creates a value of <a>RDSDataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdsDataSchemaURI</a> - The Amazon S3 location of the
--   <tt>DataSchema</tt> .</li>
--   <li><a>rdsdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon RDS <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rdsdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rdsdsDatabaseInformation</a> - Describes the
--   <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of an Amazon RDS
--   database.</li>
--   <li><a>rdsdsSelectSqlQuery</a> - The query that is used to retrieve
--   the observation data for the <tt>DataSource</tt> .</li>
--   <li><a>rdsdsDatabaseCredentials</a> - The AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon RDS
--   database.</li>
--   <li><a>rdsdsS3StagingLocation</a> - The Amazon S3 location for staging
--   Amazon RDS data. The data retrieved from Amazon RDS using
--   <tt>SelectSqlQuery</tt> is stored in this location.</li>
--   <li><a>rdsdsResourceRole</a> - The role
--   (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute
--   Cloud (Amazon EC2) instance to carry out the copy operation from
--   Amazon RDS to an Amazon S3 task. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rdsdsServiceRole</a> - The role (DataPipelineDefaultRole)
--   assumed by AWS Data Pipeline service to monitor the progress of the
--   copy task from Amazon RDS to Amazon S3. For more information, see
--   <a>Role templates</a> for data pipelines.</li>
--   <li><a>rdsdsSubnetId</a> - The subnet ID to be used to access a
--   VPC-based RDS DB instance. This attribute is used by Data Pipeline to
--   carry out the copy task from Amazon RDS to Amazon S3.</li>
--   <li><a>rdsdsSecurityGroupIds</a> - The security group IDs to be used
--   to access a VPC-based RDS DB instance. Ensure that there are
--   appropriate ingress rules set up to allow access to the RDS DB
--   instance. This attribute is used by Data Pipeline to carry out the
--   copy operation from Amazon RDS to an Amazon S3 task.</li>
--   </ul>
rdsDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec

-- | The Amazon S3 location of the <tt>DataSchema</tt> .
rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon RDS
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> Define your <tt>DataSchema</tt> as a
--   series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of
--   an Amazon RDS database.
rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase

-- | The query that is used to retrieve the observation data for the
--   <tt>DataSource</tt> .
rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text

-- | The AWS Identity and Access Management (IAM) credentials that are used
--   connect to the Amazon RDS database.
rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials

-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
--   from Amazon RDS using <tt>SelectSqlQuery</tt> is stored in this
--   location.
rdsdsS3StagingLocation :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon
--   Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
--   operation from Amazon RDS to an Amazon S3 task. For more information,
--   see <a>Role templates</a> for data pipelines.
rdsdsResourceRole :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rdsdsServiceRole :: Lens' RDSDataSpec Text

-- | The subnet ID to be used to access a VPC-based RDS DB instance. This
--   attribute is used by Data Pipeline to carry out the copy task from
--   Amazon RDS to Amazon S3.
rdsdsSubnetId :: Lens' RDSDataSpec Text

-- | The security group IDs to be used to access a VPC-based RDS DB
--   instance. Ensure that there are appropriate ingress rules set up to
--   allow access to the RDS DB instance. This attribute is used by Data
--   Pipeline to carry out the copy operation from Amazon RDS to an Amazon
--   S3 task.
rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text]

-- | The database details of an Amazon RDS database.
--   
--   <i>See:</i> <a>rdsDatabase</a> smart constructor.
data RDSDatabase

-- | Creates a value of <a>RDSDatabase</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdInstanceIdentifier</a> - The ID of an RDS DB instance.</li>
--   <li><a>rdsdDatabaseName</a> - Undocumented member.</li>
--   </ul>
rdsDatabase :: Text -> Text -> RDSDatabase

-- | The ID of an RDS DB instance.
rdsdInstanceIdentifier :: Lens' RDSDatabase Text

-- | Undocumented member.
rdsdDatabaseName :: Lens' RDSDatabase Text

-- | The database credentials to connect to a database on an RDS DB
--   instance.
--   
--   <i>See:</i> <a>rdsDatabaseCredentials</a> smart constructor.
data RDSDatabaseCredentials

-- | Creates a value of <a>RDSDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdcUsername</a> - Undocumented member.</li>
--   <li><a>rdsdcPassword</a> - Undocumented member.</li>
--   </ul>
rdsDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials

-- | Undocumented member.
rdsdcUsername :: Lens' RDSDatabaseCredentials Text

-- | Undocumented member.
rdsdcPassword :: Lens' RDSDatabaseCredentials Text

-- | The datasource details that are specific to Amazon RDS.
--   
--   <i>See:</i> <a>rdsMetadata</a> smart constructor.
data RDSMetadata

-- | Creates a value of <a>RDSMetadata</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rmSelectSqlQuery</a> - The SQL query that is supplied during
--   <tt>CreateDataSourceFromRDS</tt> . Returns only if <tt>Verbose</tt> is
--   true in <tt>GetDataSourceInput</tt> .</li>
--   <li><a>rmDataPipelineId</a> - The ID of the Data Pipeline instance
--   that is used to carry to copy data from Amazon RDS to Amazon S3. You
--   can use the ID to find details about the instance in the Data Pipeline
--   console.</li>
--   <li><a>rmDatabase</a> - The database details required to connect to an
--   Amazon RDS.</li>
--   <li><a>rmDatabaseUserName</a> - Undocumented member.</li>
--   <li><a>rmResourceRole</a> - The role (DataPipelineDefaultResourceRole)
--   assumed by an Amazon EC2 instance to carry out the copy task from
--   Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rmServiceRole</a> - The role (DataPipelineDefaultRole) assumed
--   by the Data Pipeline service to monitor the progress of the copy task
--   from Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   </ul>
rdsMetadata :: RDSMetadata

-- | The SQL query that is supplied during <tt>CreateDataSourceFromRDS</tt>
--   . Returns only if <tt>Verbose</tt> is true in
--   <tt>GetDataSourceInput</tt> .
rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text)

-- | The ID of the Data Pipeline instance that is used to carry to copy
--   data from Amazon RDS to Amazon S3. You can use the ID to find details
--   about the instance in the Data Pipeline console.
rmDataPipelineId :: Lens' RDSMetadata (Maybe Text)

-- | The database details required to connect to an Amazon RDS.
rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase)

-- | Undocumented member.
rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2
--   instance to carry out the copy task from Amazon RDS to Amazon S3. For
--   more information, see <a>Role templates</a> for data pipelines.
rmResourceRole :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultRole) assumed by the Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rmServiceRole :: Lens' RDSMetadata (Maybe Text)

-- | Describes the real-time endpoint information for an <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>realtimeEndpointInfo</a> smart constructor.
data RealtimeEndpointInfo

-- | Creates a value of <a>RealtimeEndpointInfo</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>reiCreatedAt</a> - The time that the request to create the
--   real-time endpoint for the <tt>MLModel</tt> was received. The time is
--   expressed in epoch time.</li>
--   <li><a>reiEndpointURL</a> - The URI that specifies where to send
--   real-time prediction requests for the <tt>MLModel</tt> .</li>
--   <li><a>reiEndpointStatus</a> - The current status of the real-time
--   endpoint for the <tt>MLModel</tt> . This element can have one of the
--   following values: * <tt>NONE</tt> - Endpoint does not exist or was
--   previously deleted. * <tt>READY</tt> - Endpoint is ready to be used
--   for real-time predictions. * <tt>UPDATING</tt> - Updating/creating the
--   endpoint.</li>
--   <li><a>reiPeakRequestsPerSecond</a> - The maximum processing rate for
--   the real-time endpoint for <tt>MLModel</tt> , measured in incoming
--   requests per second.</li>
--   </ul>
realtimeEndpointInfo :: RealtimeEndpointInfo

-- | The time that the request to create the real-time endpoint for the
--   <tt>MLModel</tt> was received. The time is expressed in epoch time.
reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)

-- | The URI that specifies where to send real-time prediction requests for
--   the <tt>MLModel</tt> .
reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text)

-- | The current status of the real-time endpoint for the <tt>MLModel</tt>
--   . This element can have one of the following values: * <tt>NONE</tt> -
--   Endpoint does not exist or was previously deleted. * <tt>READY</tt> -
--   Endpoint is ready to be used for real-time predictions. *
--   <tt>UPDATING</tt> - Updating/creating the endpoint.
reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)

-- | The maximum processing rate for the real-time endpoint for
--   <tt>MLModel</tt> , measured in incoming requests per second.
reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)

-- | Describes the data specification of an Amazon Redshift
--   <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>redshiftDataSpec</a> smart constructor.
data RedshiftDataSpec

-- | Creates a value of <a>RedshiftDataSpec</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rDataSchemaURI</a> - Describes the schema location for an
--   Amazon Redshift <tt>DataSource</tt> .</li>
--   <li><a>rDataSchema</a> - A JSON string that represents the schema for
--   an Amazon Redshift <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rDatabaseInformation</a> - Describes the <tt>DatabaseName</tt>
--   and <tt>ClusterIdentifier</tt> for an Amazon Redshift
--   <tt>DataSource</tt> .</li>
--   <li><a>rSelectSqlQuery</a> - Describes the SQL Query to execute on an
--   Amazon Redshift database for an Amazon Redshift <tt>DataSource</tt>
--   .</li>
--   <li><a>rDatabaseCredentials</a> - Describes AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon
--   Redshift database.</li>
--   <li><a>rS3StagingLocation</a> - Describes an Amazon S3 location to
--   store the result set of the <tt>SelectSqlQuery</tt> query.</li>
--   </ul>
redshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec

-- | Describes the schema location for an Amazon Redshift
--   <tt>DataSource</tt> .
rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon Redshift
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> . Define your <tt>DataSchema</tt> as
--   a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rDataSchema :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>ClusterIdentifier</tt> for
--   an Amazon Redshift <tt>DataSource</tt> .
rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase

-- | Describes the SQL Query to execute on an Amazon Redshift database for
--   an Amazon Redshift <tt>DataSource</tt> .
rSelectSqlQuery :: Lens' RedshiftDataSpec Text

-- | Describes AWS Identity and Access Management (IAM) credentials that
--   are used connect to the Amazon Redshift database.
rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials

-- | Describes an Amazon S3 location to store the result set of the
--   <tt>SelectSqlQuery</tt> query.
rS3StagingLocation :: Lens' RedshiftDataSpec Text

-- | Describes the database details required to connect to an Amazon
--   Redshift database.
--   
--   <i>See:</i> <a>redshiftDatabase</a> smart constructor.
data RedshiftDatabase

-- | Creates a value of <a>RedshiftDatabase</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdDatabaseName</a> - Undocumented member.</li>
--   <li><a>rdClusterIdentifier</a> - Undocumented member.</li>
--   </ul>
redshiftDatabase :: Text -> Text -> RedshiftDatabase

-- | Undocumented member.
rdDatabaseName :: Lens' RedshiftDatabase Text

-- | Undocumented member.
rdClusterIdentifier :: Lens' RedshiftDatabase Text

-- | Describes the database credentials for connecting to a database on an
--   Amazon Redshift cluster.
--   
--   <i>See:</i> <a>redshiftDatabaseCredentials</a> smart constructor.
data RedshiftDatabaseCredentials

-- | Creates a value of <a>RedshiftDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdcUsername</a> - Undocumented member.</li>
--   <li><a>rdcPassword</a> - Undocumented member.</li>
--   </ul>
redshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials

-- | Undocumented member.
rdcUsername :: Lens' RedshiftDatabaseCredentials Text

-- | Undocumented member.
rdcPassword :: Lens' RedshiftDatabaseCredentials Text

-- | Describes the <tt>DataSource</tt> details specific to Amazon Redshift.
--   
--   <i>See:</i> <a>redshiftMetadata</a> smart constructor.
data RedshiftMetadata

-- | Creates a value of <a>RedshiftMetadata</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>redSelectSqlQuery</a> - The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.</li>
--   <li><a>redRedshiftDatabase</a> - Undocumented member.</li>
--   <li><a>redDatabaseUserName</a> - Undocumented member.</li>
--   </ul>
redshiftMetadata :: RedshiftMetadata

-- | The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.
redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text)

-- | Undocumented member.
redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)

-- | Undocumented member.
redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text)

-- | Describes the data specification of a <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>s3DataSpec</a> smart constructor.
data S3DataSpec

-- | Creates a value of <a>S3DataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>sdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon S3 <tt>DataSource</tt> . The <tt>DataSchema</tt> defines
--   the structure of the observation data in the data file(s) referenced
--   in the <tt>DataSource</tt> . You must provide either the
--   <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>sdsDataSchemaLocationS3</a> - Describes the schema location in
--   Amazon S3. You must provide either the <tt>DataSchema</tt> or the
--   <tt>DataSchemaLocationS3</tt> .</li>
--   <li><a>sdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>sdsDataLocationS3</a> - The location of the data file(s) used
--   by a <tt>DataSource</tt> . The URI specifies a data file or an Amazon
--   Simple Storage Service (Amazon S3) directory or bucket containing data
--   files.</li>
--   </ul>
s3DataSpec :: Text -> S3DataSpec

-- | A JSON string that represents the schema for an Amazon S3
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . You must provide either the <tt>DataSchema</tt>
--   or the <tt>DataSchemaLocationS3</tt> . Define your <tt>DataSchema</tt>
--   as a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
sdsDataSchema :: Lens' S3DataSpec (Maybe Text)

-- | Describes the schema location in Amazon S3. You must provide either
--   the <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> .
sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text)

-- | The location of the data file(s) used by a <tt>DataSource</tt> . The
--   URI specifies a data file or an Amazon Simple Storage Service (Amazon
--   S3) directory or bucket containing data files.
sdsDataLocationS3 :: Lens' S3DataSpec Text

-- | A custom key-value pair associated with an ML object, such as an ML
--   model.
--   
--   <i>See:</i> <a>tag</a> smart constructor.
data Tag

-- | Creates a value of <a>Tag</a> with the minimum fields required to make
--   a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>tagValue</a> - An optional string, typically used to describe
--   or define the tag. Valid characters include Unicode letters, digits,
--   white space, _, ., /, =, +, -, %, and @.</li>
--   <li><a>tagKey</a> - A unique identifier for the tag. Valid characters
--   include Unicode letters, digits, white space, _, ., /, =, +, -, %, and
--   @.</li>
--   </ul>
tag :: Tag

-- | An optional string, typically used to describe or define the tag.
--   Valid characters include Unicode letters, digits, white space, _, .,
--   /, =, +, -, %, and @.
tagValue :: Lens' Tag (Maybe Text)

-- | A unique identifier for the tag. Valid characters include Unicode
--   letters, digits, white space, _, ., /, =, +, -, %, and @.
tagKey :: Lens' Tag (Maybe Text)


-- | Generates a prediction for the observation using the specified <tt>ML
--   Model</tt> .
module Network.AWS.MachineLearning.Predict

-- | Creates a value of <a>Predict</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pMLModelId</a> - A unique identifier of the <tt>MLModel</tt>
--   .</li>
--   <li><a>pRecord</a> - Undocumented member.</li>
--   <li><a>pPredictEndpoint</a> - Undocumented member.</li>
--   </ul>
predict :: Text -> Text -> Predict

-- | <i>See:</i> <a>predict</a> smart constructor.
data Predict

-- | A unique identifier of the <tt>MLModel</tt> .
pMLModelId :: Lens' Predict Text

-- | Undocumented member.
pRecord :: Lens' Predict (HashMap Text Text)

-- | Undocumented member.
pPredictEndpoint :: Lens' Predict Text

-- | Creates a value of <a>PredictResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>prsPrediction</a> - Undocumented member.</li>
--   <li><a>prsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
predictResponse :: Int -> PredictResponse

-- | <i>See:</i> <a>predictResponse</a> smart constructor.
data PredictResponse

-- | Undocumented member.
prsPrediction :: Lens' PredictResponse (Maybe Prediction)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
prsResponseStatus :: Lens' PredictResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.Predict.PredictResponse
instance Data.Data.Data Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Show.Show Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Read.Read Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.Predict.Predict
instance Data.Data.Data Network.AWS.MachineLearning.Predict.Predict
instance GHC.Show.Show Network.AWS.MachineLearning.Predict.Predict
instance GHC.Read.Read Network.AWS.MachineLearning.Predict.Predict
instance GHC.Classes.Eq Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.Predict.Predict
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.Predict.PredictResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.Predict.Predict
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.Predict.Predict
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.Predict.Predict


-- | Returns an <tt>MLModel</tt> that includes detailed metadata, data
--   source information, and the current status of the <tt>MLModel</tt> .
--   
--   <tt>GetMLModel</tt> provides results in normal or verbose format.
module Network.AWS.MachineLearning.GetMLModel

-- | Creates a value of <a>GetMLModel</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gmlmVerbose</a> - Specifies whether the <tt>GetMLModel</tt>
--   operation should return <tt>Recipe</tt> . If true, <tt>Recipe</tt> is
--   returned. If false, <tt>Recipe</tt> is not returned.</li>
--   <li><a>gmlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt> at
--   creation.</li>
--   </ul>
getMLModel :: Text -> GetMLModel

-- | <i>See:</i> <a>getMLModel</a> smart constructor.
data GetMLModel

-- | Specifies whether the <tt>GetMLModel</tt> operation should return
--   <tt>Recipe</tt> . If true, <tt>Recipe</tt> is returned. If false,
--   <tt>Recipe</tt> is not returned.
gmlmVerbose :: Lens' GetMLModel (Maybe Bool)

-- | The ID assigned to the <tt>MLModel</tt> at creation.
gmlmMLModelId :: Lens' GetMLModel Text

-- | Creates a value of <a>GetMLModelResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gmlmrsStatus</a> - The current status of the <tt>MLModel</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to describe
--   a <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The request is
--   processing. * <tt>FAILED</tt> - The request did not run to completion.
--   The ML model isn't usable. * <tt>COMPLETED</tt> - The request
--   completed successfully. * <tt>DELETED</tt> - The <tt>MLModel</tt> is
--   marked as deleted. It isn't usable.</li>
--   <li><a>gmlmrsLastUpdatedAt</a> - The time of the most recent edit to
--   the <tt>MLModel</tt> . The time is expressed in epoch time.</li>
--   <li><a>gmlmrsTrainingParameters</a> - A list of the training
--   parameters in the <tt>MLModel</tt> . The list is implemented as a map
--   of key-value pairs. The following is the current set of training
--   parameters: * <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed
--   size of the model. Depending on the input data, the size of the model
--   might affect its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling data improves a model's ability to find the optimal
--   solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>gmlmrsScoreThresholdLastUpdatedAt</a> - The time of the most
--   recent edit to the <tt>ScoreThreshold</tt> . The time is expressed in
--   epoch time.</li>
--   <li><a>gmlmrsCreatedAt</a> - The time that the <tt>MLModel</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>gmlmrsComputeTime</a> - The approximate CPU time in
--   milliseconds that Amazon Machine Learning spent processing the
--   <tt>MLModel</tt> , normalized and scaled on computation resources.
--   <tt>ComputeTime</tt> is only available if the <tt>MLModel</tt> is in
--   the <tt>COMPLETED</tt> state.</li>
--   <li><a>gmlmrsRecipe</a> - The recipe to use when training the
--   <tt>MLModel</tt> . The <tt>Recipe</tt> provides detailed information
--   about the observation data to use during training, and manipulations
--   to perform on the observation data during training.</li>
--   <li><a>gmlmrsInputDataLocationS3</a> - The location of the data file
--   or directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gmlmrsMLModelId</a> - The MLModel ID, which is same as the
--   <tt>MLModelId</tt> in the request.</li>
--   <li><a>gmlmrsSizeInBytes</a> - Undocumented member.</li>
--   <li><a>gmlmrsSchema</a> - The schema used by all of the data files
--   referenced by the <tt>DataSource</tt> .</li>
--   <li><a>gmlmrsStartedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>MLModel</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>MLModel</tt> is in the
--   <tt>PENDING</tt> state.</li>
--   <li><a>gmlmrsScoreThreshold</a> - The scoring threshold is used in
--   binary classification <tt>MLModel</tt> models. It marks the boundary
--   between a positive prediction and a negative prediction. Output values
--   greater than or equal to the threshold receive a positive result from
--   the MLModel, such as <tt>true</tt> . Output values less than the
--   threshold receive a negative response from the MLModel, such as
--   <tt>false</tt> .</li>
--   <li><a>gmlmrsFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>MLModel</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>MLModel</tt> is in the <tt>COMPLETED</tt> or <tt>FAILED</tt>
--   state.</li>
--   <li><a>gmlmrsCreatedByIAMUser</a> - The AWS user account from which
--   the <tt>MLModel</tt> was created. The account type can be either an
--   AWS root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>gmlmrsName</a> - A user-supplied name or description of the
--   <tt>MLModel</tt> .</li>
--   <li><a>gmlmrsLogURI</a> - A link to the file that contains logs of the
--   <tt>CreateMLModel</tt> operation.</li>
--   <li><a>gmlmrsEndpointInfo</a> - The current endpoint of the
--   <tt>MLModel</tt></li>
--   <li><a>gmlmrsTrainingDataSourceId</a> - The ID of the training
--   <tt>DataSource</tt> .</li>
--   <li><a>gmlmrsMessage</a> - A description of the most recent details
--   about accessing the <tt>MLModel</tt> .</li>
--   <li><a>gmlmrsMLModelType</a> - Identifies the <tt>MLModel</tt>
--   category. The following are the available types: * REGRESSION --
--   Produces a numeric result. For example, "What price should a house be
--   listed at?" * BINARY -- Produces one of two possible results. For
--   example, "Is this an e-commerce website?" * MULTICLASS -- Produces one
--   of several possible results. For example, "Is this a HIGH, LOW or
--   MEDIUM risk trade?"</li>
--   <li><a>gmlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getMLModelResponse :: Int -> GetMLModelResponse

-- | Represents the output of a <tt>GetMLModel</tt> operation, and provides
--   detailed information about a <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>getMLModelResponse</a> smart constructor.
data GetMLModelResponse

-- | The current status of the <tt>MLModel</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to describe a <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The request is processing. * <tt>FAILED</tt> -
--   The request did not run to completion. The ML model isn't usable. *
--   <tt>COMPLETED</tt> - The request completed successfully. *
--   <tt>DELETED</tt> - The <tt>MLModel</tt> is marked as deleted. It isn't
--   usable.
gmlmrsStatus :: Lens' GetMLModelResponse (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>MLModel</tt> . The time is
--   expressed in epoch time.
gmlmrsLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling data improves a model's ability to find the optimal
--   solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
gmlmrsTrainingParameters :: Lens' GetMLModelResponse (HashMap Text Text)

-- | The time of the most recent edit to the <tt>ScoreThreshold</tt> . The
--   time is expressed in epoch time.
gmlmrsScoreThresholdLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The time that the <tt>MLModel</tt> was created. The time is expressed
--   in epoch time.
gmlmrsCreatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>MLModel</tt> , normalized and scaled on
--   computation resources. <tt>ComputeTime</tt> is only available if the
--   <tt>MLModel</tt> is in the <tt>COMPLETED</tt> state.
gmlmrsComputeTime :: Lens' GetMLModelResponse (Maybe Integer)

-- | The recipe to use when training the <tt>MLModel</tt> . The
--   <tt>Recipe</tt> provides detailed information about the observation
--   data to use during training, and manipulations to perform on the
--   observation data during training.
gmlmrsRecipe :: Lens' GetMLModelResponse (Maybe Text)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gmlmrsInputDataLocationS3 :: Lens' GetMLModelResponse (Maybe Text)

-- | The MLModel ID, which is same as the <tt>MLModelId</tt> in the
--   request.
gmlmrsMLModelId :: Lens' GetMLModelResponse (Maybe Text)

-- | Undocumented member.
gmlmrsSizeInBytes :: Lens' GetMLModelResponse (Maybe Integer)

-- | The schema used by all of the data files referenced by the
--   <tt>DataSource</tt> .
gmlmrsSchema :: Lens' GetMLModelResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>MLModel</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt> isn't
--   available if the <tt>MLModel</tt> is in the <tt>PENDING</tt> state.
gmlmrsStartedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The scoring threshold is used in binary classification
--   <tt>MLModel</tt> models. It marks the boundary between a positive
--   prediction and a negative prediction. Output values greater than or
--   equal to the threshold receive a positive result from the MLModel,
--   such as <tt>true</tt> . Output values less than the threshold receive
--   a negative response from the MLModel, such as <tt>false</tt> .
gmlmrsScoreThreshold :: Lens' GetMLModelResponse (Maybe Double)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>MLModel</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the <tt>MLModel</tt> is in
--   the <tt>COMPLETED</tt> or <tt>FAILED</tt> state.
gmlmrsFinishedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The AWS user account from which the <tt>MLModel</tt> was created. The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
gmlmrsCreatedByIAMUser :: Lens' GetMLModelResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
gmlmrsName :: Lens' GetMLModelResponse (Maybe Text)

-- | A link to the file that contains logs of the <tt>CreateMLModel</tt>
--   operation.
gmlmrsLogURI :: Lens' GetMLModelResponse (Maybe Text)

-- | The current endpoint of the <tt>MLModel</tt>
gmlmrsEndpointInfo :: Lens' GetMLModelResponse (Maybe RealtimeEndpointInfo)

-- | The ID of the training <tt>DataSource</tt> .
gmlmrsTrainingDataSourceId :: Lens' GetMLModelResponse (Maybe Text)

-- | A description of the most recent details about accessing the
--   <tt>MLModel</tt> .
gmlmrsMessage :: Lens' GetMLModelResponse (Maybe Text)

-- | Identifies the <tt>MLModel</tt> category. The following are the
--   available types: * REGRESSION -- Produces a numeric result. For
--   example, "What price should a house be listed at?" * BINARY --
--   Produces one of two possible results. For example, "Is this an
--   e-commerce website?" * MULTICLASS -- Produces one of several possible
--   results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
gmlmrsMLModelType :: Lens' GetMLModelResponse (Maybe MLModelType)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gmlmrsResponseStatus :: Lens' GetMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Data.Data.Data Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetMLModel.GetMLModel


-- | Returns an <tt>Evaluation</tt> that includes metadata as well as the
--   current status of the <tt>Evaluation</tt> .
module Network.AWS.MachineLearning.GetEvaluation

-- | Creates a value of <a>GetEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>geEvaluationId</a> - The ID of the <tt>Evaluation</tt> to
--   retrieve. The evaluation of each <tt>MLModel</tt> is recorded and
--   cataloged. The ID provides the means to access the information.</li>
--   </ul>
getEvaluation :: Text -> GetEvaluation

-- | <i>See:</i> <a>getEvaluation</a> smart constructor.
data GetEvaluation

-- | The ID of the <tt>Evaluation</tt> to retrieve. The evaluation of each
--   <tt>MLModel</tt> is recorded and cataloged. The ID provides the means
--   to access the information.
geEvaluationId :: Lens' GetEvaluation Text

-- | Creates a value of <a>GetEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gersStatus</a> - The status of the evaluation. This element can
--   have one of the following values: * <tt>PENDING</tt> - Amazon Machine
--   Language (Amazon ML) submitted a request to evaluate an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The evaluation is underway.
--   * <tt>FAILED</tt> - The request to evaluate an <tt>MLModel</tt> did
--   not run to completion. It is not usable. * <tt>COMPLETED</tt> - The
--   evaluation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>Evaluation</tt> is marked as deleted. It is not usable.</li>
--   <li><a>gersPerformanceMetrics</a> - Measurements of how well the
--   <tt>MLModel</tt> performed using observations referenced by the
--   <tt>DataSource</tt> . One of the following metric is returned based on
--   the type of the <tt>MLModel</tt> : * BinaryAUC: A binary
--   <tt>MLModel</tt> uses the Area Under the Curve (AUC) technique to
--   measure performance. * RegressionRMSE: A regression <tt>MLModel</tt>
--   uses the Root Mean Square Error (RMSE) technique to measure
--   performance. RMSE measures the difference between predicted and actual
--   values for a single variable. * MulticlassAvgFScore: A multiclass
--   <tt>MLModel</tt> uses the F1 score technique to measure performance.
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .</li>
--   <li><a>gersLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>Evaluation</tt> . The time is expressed in epoch time.</li>
--   <li><a>gersCreatedAt</a> - The time that the <tt>Evaluation</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>gersComputeTime</a> - The approximate CPU time in milliseconds
--   that Amazon Machine Learning spent processing the <tt>Evaluation</tt>
--   , normalized and scaled on computation resources. <tt>ComputeTime</tt>
--   is only available if the <tt>Evaluation</tt> is in the
--   <tt>COMPLETED</tt> state.</li>
--   <li><a>gersInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gersMLModelId</a> - The ID of the <tt>MLModel</tt> that was the
--   focus of the evaluation.</li>
--   <li><a>gersStartedAt</a> - The epoch time when Amazon Machine Learning
--   marked the <tt>Evaluation</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>Evaluation</tt> is in
--   the <tt>PENDING</tt> state.</li>
--   <li><a>gersFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>Evaluation</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>Evaluation</tt> is in the <tt>COMPLETED</tt> or <tt>FAILED</tt>
--   state.</li>
--   <li><a>gersCreatedByIAMUser</a> - The AWS user account that invoked
--   the evaluation. The account type can be either an AWS root account or
--   an AWS Identity and Access Management (IAM) user account.</li>
--   <li><a>gersName</a> - A user-supplied name or description of the
--   <tt>Evaluation</tt> .</li>
--   <li><a>gersLogURI</a> - A link to the file that contains logs of the
--   <tt>CreateEvaluation</tt> operation.</li>
--   <li><a>gersEvaluationId</a> - The evaluation ID which is same as the
--   <tt>EvaluationId</tt> in the request.</li>
--   <li><a>gersMessage</a> - A description of the most recent details
--   about evaluating the <tt>MLModel</tt> .</li>
--   <li><a>gersEvaluationDataSourceId</a> - The <tt>DataSource</tt> used
--   for this evaluation.</li>
--   <li><a>gersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getEvaluationResponse :: Int -> GetEvaluationResponse

-- | Represents the output of a <tt>GetEvaluation</tt> operation and
--   describes an <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>getEvaluationResponse</a> smart constructor.
data GetEvaluationResponse

-- | The status of the evaluation. This element can have one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Language (Amazon
--   ML) submitted a request to evaluate an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The evaluation is underway. * <tt>FAILED</tt> -
--   The request to evaluate an <tt>MLModel</tt> did not run to completion.
--   It is not usable. * <tt>COMPLETED</tt> - The evaluation process
--   completed successfully. * <tt>DELETED</tt> - The <tt>Evaluation</tt>
--   is marked as deleted. It is not usable.
gersStatus :: Lens' GetEvaluationResponse (Maybe EntityStatus)

-- | Measurements of how well the <tt>MLModel</tt> performed using
--   observations referenced by the <tt>DataSource</tt> . One of the
--   following metric is returned based on the type of the <tt>MLModel</tt>
--   : * BinaryAUC: A binary <tt>MLModel</tt> uses the Area Under the Curve
--   (AUC) technique to measure performance. * RegressionRMSE: A regression
--   <tt>MLModel</tt> uses the Root Mean Square Error (RMSE) technique to
--   measure performance. RMSE measures the difference between predicted
--   and actual values for a single variable. * MulticlassAvgFScore: A
--   multiclass <tt>MLModel</tt> uses the F1 score technique to measure
--   performance. For more information about performance metrics, please
--   see the <a>Amazon Machine Learning Developer Guide</a> .
gersPerformanceMetrics :: Lens' GetEvaluationResponse (Maybe PerformanceMetrics)

-- | The time of the most recent edit to the <tt>Evaluation</tt> . The time
--   is expressed in epoch time.
gersLastUpdatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The time that the <tt>Evaluation</tt> was created. The time is
--   expressed in epoch time.
gersCreatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>Evaluation</tt> , normalized and scaled on
--   computation resources. <tt>ComputeTime</tt> is only available if the
--   <tt>Evaluation</tt> is in the <tt>COMPLETED</tt> state.
gersComputeTime :: Lens' GetEvaluationResponse (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gersInputDataLocationS3 :: Lens' GetEvaluationResponse (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that was the focus of the evaluation.
gersMLModelId :: Lens' GetEvaluationResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>Evaluation</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt> isn't
--   available if the <tt>Evaluation</tt> is in the <tt>PENDING</tt> state.
gersStartedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>Evaluation</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the <tt>Evaluation</tt> is
--   in the <tt>COMPLETED</tt> or <tt>FAILED</tt> state.
gersFinishedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The AWS user account that invoked the evaluation. The account type can
--   be either an AWS root account or an AWS Identity and Access Management
--   (IAM) user account.
gersCreatedByIAMUser :: Lens' GetEvaluationResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
gersName :: Lens' GetEvaluationResponse (Maybe Text)

-- | A link to the file that contains logs of the <tt>CreateEvaluation</tt>
--   operation.
gersLogURI :: Lens' GetEvaluationResponse (Maybe Text)

-- | The evaluation ID which is same as the <tt>EvaluationId</tt> in the
--   request.
gersEvaluationId :: Lens' GetEvaluationResponse (Maybe Text)

-- | A description of the most recent details about evaluating the
--   <tt>MLModel</tt> .
gersMessage :: Lens' GetEvaluationResponse (Maybe Text)

-- | The <tt>DataSource</tt> used for this evaluation.
gersEvaluationDataSourceId :: Lens' GetEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gersResponseStatus :: Lens' GetEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetEvaluation.GetEvaluation


-- | Returns a <tt>DataSource</tt> that includes metadata and data file
--   information, as well as the current status of the <tt>DataSource</tt>
--   .
--   
--   <tt>GetDataSource</tt> provides results in normal or verbose format.
--   The verbose format adds the schema description and the list of files
--   pointed to by the DataSource to the normal format.
module Network.AWS.MachineLearning.GetDataSource

-- | Creates a value of <a>GetDataSource</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gdsVerbose</a> - Specifies whether the <tt>GetDataSource</tt>
--   operation should return <tt>DataSourceSchema</tt> . If true,
--   <tt>DataSourceSchema</tt> is returned. If false,
--   <tt>DataSourceSchema</tt> is not returned.</li>
--   <li><a>gdsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> at creation.</li>
--   </ul>
getDataSource :: Text -> GetDataSource

-- | <i>See:</i> <a>getDataSource</a> smart constructor.
data GetDataSource

-- | Specifies whether the <tt>GetDataSource</tt> operation should return
--   <tt>DataSourceSchema</tt> . If true, <tt>DataSourceSchema</tt> is
--   returned. If false, <tt>DataSourceSchema</tt> is not returned.
gdsVerbose :: Lens' GetDataSource (Maybe Bool)

-- | The ID assigned to the <tt>DataSource</tt> at creation.
gdsDataSourceId :: Lens' GetDataSource Text

-- | Creates a value of <a>GetDataSourceResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gdsrsStatus</a> - The current status of the <tt>DataSource</tt>
--   . This element can have one of the following values: *
--   <tt>PENDING</tt> - Amazon ML submitted a request to create a
--   <tt>DataSource</tt> . * <tt>INPROGRESS</tt> - The creation process is
--   underway. * <tt>FAILED</tt> - The request to create a
--   <tt>DataSource</tt> did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The creation process completed successfully. *
--   <tt>DELETED</tt> - The <tt>DataSource</tt> is marked as deleted. It is
--   not usable.</li>
--   <li><a>gdsrsNumberOfFiles</a> - The number of data files referenced by
--   the <tt>DataSource</tt> .</li>
--   <li><a>gdsrsLastUpdatedAt</a> - The time of the most recent edit to
--   the <tt>DataSource</tt> . The time is expressed in epoch time.</li>
--   <li><a>gdsrsCreatedAt</a> - The time that the <tt>DataSource</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>gdsrsComputeTime</a> - The approximate CPU time in milliseconds
--   that Amazon Machine Learning spent processing the <tt>DataSource</tt>
--   , normalized and scaled on computation resources. <tt>ComputeTime</tt>
--   is only available if the <tt>DataSource</tt> is in the
--   <tt>COMPLETED</tt> state and the <tt>ComputeStatistics</tt> is set to
--   true.</li>
--   <li><a>gdsrsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> at creation. This value should be identical to the
--   value of the <tt>DataSourceId</tt> in the request.</li>
--   <li><a>gdsrsRDSMetadata</a> - Undocumented member.</li>
--   <li><a>gdsrsDataSizeInBytes</a> - The total size of observations in
--   the data files.</li>
--   <li><a>gdsrsDataSourceSchema</a> - The schema used by all of the data
--   files of this <tt>DataSource</tt> .</li>
--   <li><a>gdsrsStartedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>DataSource</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>DataSource</tt> is in
--   the <tt>PENDING</tt> state.</li>
--   <li><a>gdsrsFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>DataSource</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>DataSource</tt> is in the <tt>COMPLETED</tt> or <tt>FAILED</tt>
--   state.</li>
--   <li><a>gdsrsCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>DataSource</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>gdsrsName</a> - A user-supplied name or description of the
--   <tt>DataSource</tt> .</li>
--   <li><a>gdsrsLogURI</a> - A link to the file containing logs of
--   <tt>CreateDataSourceFrom*</tt> operations.</li>
--   <li><a>gdsrsDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gdsrsComputeStatistics</a> - The parameter is <tt>true</tt> if
--   statistics need to be generated from the observation data.</li>
--   <li><a>gdsrsMessage</a> - The user-supplied description of the most
--   recent details about creating the <tt>DataSource</tt> .</li>
--   <li><a>gdsrsRedshiftMetadata</a> - Undocumented member.</li>
--   <li><a>gdsrsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement requirement used when this
--   <tt>DataSource</tt> was created.</li>
--   <li><a>gdsrsRoleARN</a> - Undocumented member.</li>
--   <li><a>gdsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getDataSourceResponse :: Int -> GetDataSourceResponse

-- | Represents the output of a <tt>GetDataSource</tt> operation and
--   describes a <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>getDataSourceResponse</a> smart constructor.
data GetDataSourceResponse

-- | The current status of the <tt>DataSource</tt> . This element can have
--   one of the following values: * <tt>PENDING</tt> - Amazon ML submitted
--   a request to create a <tt>DataSource</tt> . * <tt>INPROGRESS</tt> -
--   The creation process is underway. * <tt>FAILED</tt> - The request to
--   create a <tt>DataSource</tt> did not run to completion. It is not
--   usable. * <tt>COMPLETED</tt> - The creation process completed
--   successfully. * <tt>DELETED</tt> - The <tt>DataSource</tt> is marked
--   as deleted. It is not usable.
gdsrsStatus :: Lens' GetDataSourceResponse (Maybe EntityStatus)

-- | The number of data files referenced by the <tt>DataSource</tt> .
gdsrsNumberOfFiles :: Lens' GetDataSourceResponse (Maybe Integer)

-- | The time of the most recent edit to the <tt>DataSource</tt> . The time
--   is expressed in epoch time.
gdsrsLastUpdatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The time that the <tt>DataSource</tt> was created. The time is
--   expressed in epoch time.
gdsrsCreatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>DataSource</tt> , normalized and scaled on
--   computation resources. <tt>ComputeTime</tt> is only available if the
--   <tt>DataSource</tt> is in the <tt>COMPLETED</tt> state and the
--   <tt>ComputeStatistics</tt> is set to true.
gdsrsComputeTime :: Lens' GetDataSourceResponse (Maybe Integer)

-- | The ID assigned to the <tt>DataSource</tt> at creation. This value
--   should be identical to the value of the <tt>DataSourceId</tt> in the
--   request.
gdsrsDataSourceId :: Lens' GetDataSourceResponse (Maybe Text)

-- | Undocumented member.
gdsrsRDSMetadata :: Lens' GetDataSourceResponse (Maybe RDSMetadata)

-- | The total size of observations in the data files.
gdsrsDataSizeInBytes :: Lens' GetDataSourceResponse (Maybe Integer)

-- | The schema used by all of the data files of this <tt>DataSource</tt> .
gdsrsDataSourceSchema :: Lens' GetDataSourceResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>DataSource</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt> isn't
--   available if the <tt>DataSource</tt> is in the <tt>PENDING</tt> state.
gdsrsStartedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>DataSource</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the <tt>DataSource</tt> is
--   in the <tt>COMPLETED</tt> or <tt>FAILED</tt> state.
gdsrsFinishedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The AWS user account from which the <tt>DataSource</tt> was created.
--   The account type can be either an AWS root account or an AWS Identity
--   and Access Management (IAM) user account.
gdsrsCreatedByIAMUser :: Lens' GetDataSourceResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>DataSource</tt> .
gdsrsName :: Lens' GetDataSourceResponse (Maybe Text)

-- | A link to the file containing logs of <tt>CreateDataSourceFrom*</tt>
--   operations.
gdsrsLogURI :: Lens' GetDataSourceResponse (Maybe Text)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gdsrsDataLocationS3 :: Lens' GetDataSourceResponse (Maybe Text)

-- | The parameter is <tt>true</tt> if statistics need to be generated from
--   the observation data.
gdsrsComputeStatistics :: Lens' GetDataSourceResponse (Maybe Bool)

-- | The user-supplied description of the most recent details about
--   creating the <tt>DataSource</tt> .
gdsrsMessage :: Lens' GetDataSourceResponse (Maybe Text)

-- | Undocumented member.
gdsrsRedshiftMetadata :: Lens' GetDataSourceResponse (Maybe RedshiftMetadata)

-- | A JSON string that represents the splitting and rearrangement
--   requirement used when this <tt>DataSource</tt> was created.
gdsrsDataRearrangement :: Lens' GetDataSourceResponse (Maybe Text)

-- | Undocumented member.
gdsrsRoleARN :: Lens' GetDataSourceResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gdsrsResponseStatus :: Lens' GetDataSourceResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Data.Data.Data Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance GHC.Show.Show Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance GHC.Read.Read Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetDataSource.GetDataSource


-- | Returns a <tt>BatchPrediction</tt> that includes detailed metadata,
--   status, and data file information for a <tt>Batch Prediction</tt>
--   request.
module Network.AWS.MachineLearning.GetBatchPrediction

-- | Creates a value of <a>GetBatchPrediction</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gbpBatchPredictionId</a> - An ID assigned to the
--   <tt>BatchPrediction</tt> at creation.</li>
--   </ul>
getBatchPrediction :: Text -> GetBatchPrediction

-- | <i>See:</i> <a>getBatchPrediction</a> smart constructor.
data GetBatchPrediction

-- | An ID assigned to the <tt>BatchPrediction</tt> at creation.
gbpBatchPredictionId :: Lens' GetBatchPrediction Text

-- | Creates a value of <a>GetBatchPredictionResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gbprsStatus</a> - The status of the <tt>BatchPrediction</tt> ,
--   which can be one of the following values: * <tt>PENDING</tt> - Amazon
--   Machine Learning (Amazon ML) submitted a request to generate batch
--   predictions. * <tt>INPROGRESS</tt> - The batch predictions are in
--   progress. * <tt>FAILED</tt> - The request to perform a batch
--   prediction did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The batch prediction process completed
--   successfully. * <tt>DELETED</tt> - The <tt>BatchPrediction</tt> is
--   marked as deleted. It is not usable.</li>
--   <li><a>gbprsLastUpdatedAt</a> - The time of the most recent edit to
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>gbprsCreatedAt</a> - The time when the <tt>BatchPrediction</tt>
--   was created. The time is expressed in epoch time.</li>
--   <li><a>gbprsComputeTime</a> - The approximate CPU time in milliseconds
--   that Amazon Machine Learning spent processing the
--   <tt>BatchPrediction</tt> , normalized and scaled on computation
--   resources. <tt>ComputeTime</tt> is only available if the
--   <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> state.</li>
--   <li><a>gbprsInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gbprsMLModelId</a> - The ID of the <tt>MLModel</tt> that
--   generated predictions for the <tt>BatchPrediction</tt> request.</li>
--   <li><a>gbprsBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that was used to create the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsTotalRecordCount</a> - The number of total records that
--   Amazon Machine Learning saw while processing the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsStartedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>BatchPrediction</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>BatchPrediction</tt> is
--   in the <tt>PENDING</tt> state.</li>
--   <li><a>gbprsBatchPredictionId</a> - An ID assigned to the
--   <tt>BatchPrediction</tt> at creation. This value should be identical
--   to the value of the <tt>BatchPredictionID</tt> in the request.</li>
--   <li><a>gbprsFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>BatchPrediction</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> state.</li>
--   <li><a>gbprsInvalidRecordCount</a> - The number of invalid records
--   that Amazon Machine Learning saw while processing the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsCreatedByIAMUser</a> - The AWS user account that invoked
--   the <tt>BatchPrediction</tt> . The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>gbprsName</a> - A user-supplied name or description of the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsLogURI</a> - A link to the file that contains logs of the
--   <tt>CreateBatchPrediction</tt> operation.</li>
--   <li><a>gbprsMessage</a> - A description of the most recent details
--   about processing the batch prediction request.</li>
--   <li><a>gbprsOutputURI</a> - The location of an Amazon S3 bucket or
--   directory to receive the operation results.</li>
--   <li><a>gbprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getBatchPredictionResponse :: Int -> GetBatchPredictionResponse

-- | Represents the output of a <tt>GetBatchPrediction</tt> operation and
--   describes a <tt>BatchPrediction</tt> .
--   
--   <i>See:</i> <a>getBatchPredictionResponse</a> smart constructor.
data GetBatchPredictionResponse

-- | The status of the <tt>BatchPrediction</tt> , which can be one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Learning (Amazon
--   ML) submitted a request to generate batch predictions. *
--   <tt>INPROGRESS</tt> - The batch predictions are in progress. *
--   <tt>FAILED</tt> - The request to perform a batch prediction did not
--   run to completion. It is not usable. * <tt>COMPLETED</tt> - The batch
--   prediction process completed successfully. * <tt>DELETED</tt> - The
--   <tt>BatchPrediction</tt> is marked as deleted. It is not usable.
gbprsStatus :: Lens' GetBatchPredictionResponse (Maybe EntityStatus)

-- | The time of the most recent edit to <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
gbprsLastUpdatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | The time when the <tt>BatchPrediction</tt> was created. The time is
--   expressed in epoch time.
gbprsCreatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>BatchPrediction</tt> , normalized and scaled
--   on computation resources. <tt>ComputeTime</tt> is only available if
--   the <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> state.
gbprsComputeTime :: Lens' GetBatchPredictionResponse (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gbprsInputDataLocationS3 :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that generated predictions for the
--   <tt>BatchPrediction</tt> request.
gbprsMLModelId :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that was used to create the
--   <tt>BatchPrediction</tt> .
gbprsBatchPredictionDataSourceId :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The number of total records that Amazon Machine Learning saw while
--   processing the <tt>BatchPrediction</tt> .
gbprsTotalRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>BatchPrediction</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt>
--   isn't available if the <tt>BatchPrediction</tt> is in the
--   <tt>PENDING</tt> state.
gbprsStartedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | An ID assigned to the <tt>BatchPrediction</tt> at creation. This value
--   should be identical to the value of the <tt>BatchPredictionID</tt> in
--   the request.
gbprsBatchPredictionId :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>BatchPrediction</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the
--   <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> state.
gbprsFinishedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | The number of invalid records that Amazon Machine Learning saw while
--   processing the <tt>BatchPrediction</tt> .
gbprsInvalidRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)

-- | The AWS user account that invoked the <tt>BatchPrediction</tt> . The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
gbprsCreatedByIAMUser :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
gbprsName :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | A link to the file that contains logs of the
--   <tt>CreateBatchPrediction</tt> operation.
gbprsLogURI :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | A description of the most recent details about processing the batch
--   prediction request.
gbprsMessage :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The location of an Amazon S3 bucket or directory to receive the
--   operation results.
gbprsOutputURI :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gbprsResponseStatus :: Lens' GetBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction


-- | Describes one or more of the tags for your Amazon ML object.
module Network.AWS.MachineLearning.DescribeTags

-- | Creates a value of <a>DescribeTags</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dtResourceId</a> - The ID of the ML object. For example,
--   <tt>exampleModelId</tt> .</li>
--   <li><a>dtResourceType</a> - The type of the ML object.</li>
--   </ul>
describeTags :: Text -> TaggableResourceType -> DescribeTags

-- | <i>See:</i> <a>describeTags</a> smart constructor.
data DescribeTags

-- | The ID of the ML object. For example, <tt>exampleModelId</tt> .
dtResourceId :: Lens' DescribeTags Text

-- | The type of the ML object.
dtResourceType :: Lens' DescribeTags TaggableResourceType

-- | Creates a value of <a>DescribeTagsResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dtrsResourceId</a> - The ID of the tagged ML object.</li>
--   <li><a>dtrsResourceType</a> - The type of the tagged ML object.</li>
--   <li><a>dtrsTags</a> - A list of tags associated with the ML
--   object.</li>
--   <li><a>dtrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeTagsResponse :: Int -> DescribeTagsResponse

-- | Amazon ML returns the following elements.
--   
--   <i>See:</i> <a>describeTagsResponse</a> smart constructor.
data DescribeTagsResponse

-- | The ID of the tagged ML object.
dtrsResourceId :: Lens' DescribeTagsResponse (Maybe Text)

-- | The type of the tagged ML object.
dtrsResourceType :: Lens' DescribeTagsResponse (Maybe TaggableResourceType)

-- | A list of tags associated with the ML object.
dtrsTags :: Lens' DescribeTagsResponse [Tag]

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dtrsResponseStatus :: Lens' DescribeTagsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Data.Data.Data Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeTags.DescribeTags


-- | Returns a list of <tt>MLModel</tt> that match the search criteria in
--   the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeMLModels

-- | Creates a value of <a>DescribeMLModels</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmEQ</a> - The equal to operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>dmlmGE</a> - The greater than or equal to operator. The
--   <tt>MLModel</tt> results will have <tt>FilterVariable</tt> values that
--   are greater than or equal to the value specified with <tt>GE</tt>
--   .</li>
--   <li><a>dmlmPrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, an
--   <tt>MLModel</tt> could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>MLModel</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>dmlmGT</a> - The greater than operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   the value specified with <tt>GT</tt> .</li>
--   <li><a>dmlmNE</a> - The not equal to operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values not equal to the
--   value specified with <tt>NE</tt> .</li>
--   <li><a>dmlmNextToken</a> - The ID of the page in the paginated
--   results.</li>
--   <li><a>dmlmSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>MLModel</tt> . * <tt>asc</tt> -
--   Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>dmlmLimit</a> - The number of pages of information to include
--   in the result. The range of acceptable values is <tt>1</tt> through
--   <tt>100</tt> . The default value is <tt>100</tt> .</li>
--   <li><a>dmlmLT</a> - The less than operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values that are less than
--   the value specified with <tt>LT</tt> .</li>
--   <li><a>dmlmFilterVariable</a> - Use one of the following variables to
--   filter a list of <tt>MLModel</tt> : * <tt>CreatedAt</tt> - Sets the
--   search criteria to <tt>MLModel</tt> creation date. * <tt>Status</tt> -
--   Sets the search criteria to <tt>MLModel</tt> status. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>MLModel</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked the <tt>MLModel</tt> creation. *
--   <tt>TrainingDataSourceId</tt> - Sets the search criteria to the
--   <tt>DataSource</tt> used to train one or more <tt>MLModel</tt> . *
--   <tt>RealtimeEndpointStatus</tt> - Sets the search criteria to the
--   <tt>MLModel</tt> real-time endpoint status. * <tt>MLModelType</tt> -
--   Sets the search criteria to <tt>MLModel</tt> type: binary, regression,
--   or multi-class. * <tt>Algorithm</tt> - Sets the search criteria to the
--   algorithm that the <tt>MLModel</tt> uses. * <tt>TrainingDataURI</tt> -
--   Sets the search criteria to the data file(s) used in training a
--   <tt>MLModel</tt> . The URL can identify either a file or an Amazon
--   Simple Storage Service (Amazon S3) bucket or directory.</li>
--   <li><a>dmlmLE</a> - The less than or equal to operator. The
--   <tt>MLModel</tt> results will have <tt>FilterVariable</tt> values that
--   are less than or equal to the value specified with <tt>LE</tt> .</li>
--   </ul>
describeMLModels :: DescribeMLModels

-- | <i>See:</i> <a>describeMLModels</a> smart constructor.
data DescribeMLModels

-- | The equal to operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
dmlmEQ :: Lens' DescribeMLModels (Maybe Text)

-- | The greater than or equal to operator. The <tt>MLModel</tt> results
--   will have <tt>FilterVariable</tt> values that are greater than or
--   equal to the value specified with <tt>GE</tt> .
dmlmGE :: Lens' DescribeMLModels (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, an <tt>MLModel</tt> could
--   have the <tt>Name</tt> <tt>2014-09-09-HolidayGiftMailer</tt> . To
--   search for this <tt>MLModel</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
dmlmPrefix :: Lens' DescribeMLModels (Maybe Text)

-- | The greater than operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
dmlmGT :: Lens' DescribeMLModels (Maybe Text)

-- | The not equal to operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values not equal to the value specified with
--   <tt>NE</tt> .
dmlmNE :: Lens' DescribeMLModels (Maybe Text)

-- | The ID of the page in the paginated results.
dmlmNextToken :: Lens' DescribeMLModels (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>MLModel</tt> . * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
dmlmSortOrder :: Lens' DescribeMLModels (Maybe SortOrder)

-- | The number of pages of information to include in the result. The range
--   of acceptable values is <tt>1</tt> through <tt>100</tt> . The default
--   value is <tt>100</tt> .
dmlmLimit :: Lens' DescribeMLModels (Maybe Natural)

-- | The less than operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
dmlmLT :: Lens' DescribeMLModels (Maybe Text)

-- | Use one of the following variables to filter a list of
--   <tt>MLModel</tt> : * <tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>MLModel</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>MLModel</tt> status. * <tt>Name</tt> - Sets the search
--   criteria to the contents of <tt>MLModel</tt> ____ <tt>Name</tt> . *
--   <tt>IAMUser</tt> - Sets the search criteria to the user account that
--   invoked the <tt>MLModel</tt> creation. * <tt>TrainingDataSourceId</tt>
--   - Sets the search criteria to the <tt>DataSource</tt> used to train
--   one or more <tt>MLModel</tt> . * <tt>RealtimeEndpointStatus</tt> -
--   Sets the search criteria to the <tt>MLModel</tt> real-time endpoint
--   status. * <tt>MLModelType</tt> - Sets the search criteria to
--   <tt>MLModel</tt> type: binary, regression, or multi-class. *
--   <tt>Algorithm</tt> - Sets the search criteria to the algorithm that
--   the <tt>MLModel</tt> uses. * <tt>TrainingDataURI</tt> - Sets the
--   search criteria to the data file(s) used in training a
--   <tt>MLModel</tt> . The URL can identify either a file or an Amazon
--   Simple Storage Service (Amazon S3) bucket or directory.
dmlmFilterVariable :: Lens' DescribeMLModels (Maybe MLModelFilterVariable)

-- | The less than or equal to operator. The <tt>MLModel</tt> results will
--   have <tt>FilterVariable</tt> values that are less than or equal to the
--   value specified with <tt>LE</tt> .
dmlmLE :: Lens' DescribeMLModels (Maybe Text)

-- | Creates a value of <a>DescribeMLModelsResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmsrsResults</a> - A list of <tt>MLModel</tt> that meet the
--   search criteria.</li>
--   <li><a>dmlmsrsNextToken</a> - The ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>dmlmsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeMLModelsResponse :: Int -> DescribeMLModelsResponse

-- | Represents the output of a <tt>DescribeMLModels</tt> operation. The
--   content is essentially a list of <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>describeMLModelsResponse</a> smart constructor.
data DescribeMLModelsResponse

-- | A list of <tt>MLModel</tt> that meet the search criteria.
dmlmsrsResults :: Lens' DescribeMLModelsResponse [MLModel]

-- | The ID of the next page in the paginated results that indicates at
--   least one more page follows.
dmlmsrsNextToken :: Lens' DescribeMLModelsResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dmlmsrsResponseStatus :: Lens' DescribeMLModelsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Data.Data Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels


-- | Returns a list of <tt>DescribeEvaluations</tt> that match the search
--   criteria in the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeEvaluations

-- | Creates a value of <a>DescribeEvaluations</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>deEQ</a> - The equal to operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>deGE</a> - The greater than or equal to operator. The
--   <tt>Evaluation</tt> results will have <tt>FilterVariable</tt> values
--   that are greater than or equal to the value specified with <tt>GE</tt>
--   .</li>
--   <li><a>dePrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, an
--   <tt>Evaluation</tt> could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>Evaluation</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>deGT</a> - The greater than operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   the value specified with <tt>GT</tt> .</li>
--   <li><a>deNE</a> - The not equal to operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values not equal to the
--   value specified with <tt>NE</tt> .</li>
--   <li><a>deNextToken</a> - The ID of the page in the paginated
--   results.</li>
--   <li><a>deSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>Evaluation</tt> . * <tt>asc</tt>
--   - Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>deLimit</a> - The maximum number of <tt>Evaluation</tt> to
--   include in the result.</li>
--   <li><a>deLT</a> - The less than operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values that are less than
--   the value specified with <tt>LT</tt> .</li>
--   <li><a>deFilterVariable</a> - Use one of the following variable to
--   filter a list of <tt>Evaluation</tt> objects: * <tt>CreatedAt</tt> -
--   Sets the search criteria to the <tt>Evaluation</tt> creation date. *
--   <tt>Status</tt> - Sets the search criteria to the <tt>Evaluation</tt>
--   status. * <tt>Name</tt> - Sets the search criteria to the contents of
--   <tt>Evaluation</tt> ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the
--   search criteria to the user account that invoked an
--   <tt>Evaluation</tt> . * <tt>MLModelId</tt> - Sets the search criteria
--   to the <tt>MLModel</tt> that was evaluated. * <tt>DataSourceId</tt> -
--   Sets the search criteria to the <tt>DataSource</tt> used in
--   <tt>Evaluation</tt> . * <tt>DataUri</tt> - Sets the search criteria to
--   the data file(s) used in <tt>Evaluation</tt> . The URL can identify
--   either a file or an Amazon Simple Storage Solution (Amazon S3) bucket
--   or directory.</li>
--   <li><a>deLE</a> - The less than or equal to operator. The
--   <tt>Evaluation</tt> results will have <tt>FilterVariable</tt> values
--   that are less than or equal to the value specified with <tt>LE</tt>
--   .</li>
--   </ul>
describeEvaluations :: DescribeEvaluations

-- | <i>See:</i> <a>describeEvaluations</a> smart constructor.
data DescribeEvaluations

-- | The equal to operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
deEQ :: Lens' DescribeEvaluations (Maybe Text)

-- | The greater than or equal to operator. The <tt>Evaluation</tt> results
--   will have <tt>FilterVariable</tt> values that are greater than or
--   equal to the value specified with <tt>GE</tt> .
deGE :: Lens' DescribeEvaluations (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, an <tt>Evaluation</tt>
--   could have the <tt>Name</tt> <tt>2014-09-09-HolidayGiftMailer</tt> .
--   To search for this <tt>Evaluation</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
dePrefix :: Lens' DescribeEvaluations (Maybe Text)

-- | The greater than operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
deGT :: Lens' DescribeEvaluations (Maybe Text)

-- | The not equal to operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values not equal to the value specified with
--   <tt>NE</tt> .
deNE :: Lens' DescribeEvaluations (Maybe Text)

-- | The ID of the page in the paginated results.
deNextToken :: Lens' DescribeEvaluations (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>Evaluation</tt> . * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
deSortOrder :: Lens' DescribeEvaluations (Maybe SortOrder)

-- | The maximum number of <tt>Evaluation</tt> to include in the result.
deLimit :: Lens' DescribeEvaluations (Maybe Natural)

-- | The less than operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
deLT :: Lens' DescribeEvaluations (Maybe Text)

-- | Use one of the following variable to filter a list of
--   <tt>Evaluation</tt> objects: * <tt>CreatedAt</tt> - Sets the search
--   criteria to the <tt>Evaluation</tt> creation date. * <tt>Status</tt> -
--   Sets the search criteria to the <tt>Evaluation</tt> status. *
--   <tt>Name</tt> - Sets the search criteria to the contents of
--   <tt>Evaluation</tt> ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the
--   search criteria to the user account that invoked an
--   <tt>Evaluation</tt> . * <tt>MLModelId</tt> - Sets the search criteria
--   to the <tt>MLModel</tt> that was evaluated. * <tt>DataSourceId</tt> -
--   Sets the search criteria to the <tt>DataSource</tt> used in
--   <tt>Evaluation</tt> . * <tt>DataUri</tt> - Sets the search criteria to
--   the data file(s) used in <tt>Evaluation</tt> . The URL can identify
--   either a file or an Amazon Simple Storage Solution (Amazon S3) bucket
--   or directory.
deFilterVariable :: Lens' DescribeEvaluations (Maybe EvaluationFilterVariable)

-- | The less than or equal to operator. The <tt>Evaluation</tt> results
--   will have <tt>FilterVariable</tt> values that are less than or equal
--   to the value specified with <tt>LE</tt> .
deLE :: Lens' DescribeEvaluations (Maybe Text)

-- | Creates a value of <a>DescribeEvaluationsResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>desrsResults</a> - A list of <tt>Evaluation</tt> that meet the
--   search criteria.</li>
--   <li><a>desrsNextToken</a> - The ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>desrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeEvaluationsResponse :: Int -> DescribeEvaluationsResponse

-- | Represents the query results from a <tt>DescribeEvaluations</tt>
--   operation. The content is essentially a list of <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>describeEvaluationsResponse</a> smart constructor.
data DescribeEvaluationsResponse

-- | A list of <tt>Evaluation</tt> that meet the search criteria.
desrsResults :: Lens' DescribeEvaluationsResponse [Evaluation]

-- | The ID of the next page in the paginated results that indicates at
--   least one more page follows.
desrsNextToken :: Lens' DescribeEvaluationsResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
desrsResponseStatus :: Lens' DescribeEvaluationsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Data.Data Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations


-- | Returns a list of <tt>DataSource</tt> that match the search criteria
--   in the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeDataSources

-- | Creates a value of <a>DescribeDataSources</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddsEQ</a> - The equal to operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>ddsGE</a> - The greater than or equal to operator. The
--   <tt>DataSource</tt> results will have <tt>FilterVariable</tt> values
--   that are greater than or equal to the value specified with <tt>GE</tt>
--   .</li>
--   <li><a>ddsPrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, a
--   <tt>DataSource</tt> could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>DataSource</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>ddsGT</a> - The greater than operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   the value specified with <tt>GT</tt> .</li>
--   <li><a>ddsNE</a> - The not equal to operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values not equal to the
--   value specified with <tt>NE</tt> .</li>
--   <li><a>ddsNextToken</a> - The ID of the page in the paginated
--   results.</li>
--   <li><a>ddsSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>DataSource</tt> . * <tt>asc</tt>
--   - Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>ddsLimit</a> - The maximum number of <tt>DataSource</tt> to
--   include in the result.</li>
--   <li><a>ddsLT</a> - The less than operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values that are less than
--   the value specified with <tt>LT</tt> .</li>
--   <li><a>ddsFilterVariable</a> - Use one of the following variables to
--   filter a list of <tt>DataSource</tt> : * <tt>CreatedAt</tt> - Sets the
--   search criteria to <tt>DataSource</tt> creation dates. *
--   <tt>Status</tt> - Sets the search criteria to <tt>DataSource</tt>
--   statuses. * <tt>Name</tt> - Sets the search criteria to the contents
--   of <tt>DataSource</tt> ____ <tt>Name</tt> . * <tt>DataUri</tt> - Sets
--   the search criteria to the URI of data files used to create the
--   <tt>DataSource</tt> . The URI can identify either a file or an Amazon
--   Simple Storage Service (Amazon S3) bucket or directory. *
--   <tt>IAMUser</tt> - Sets the search criteria to the user account that
--   invoked the <tt>DataSource</tt> creation.</li>
--   <li><a>ddsLE</a> - The less than or equal to operator. The
--   <tt>DataSource</tt> results will have <tt>FilterVariable</tt> values
--   that are less than or equal to the value specified with <tt>LE</tt>
--   .</li>
--   </ul>
describeDataSources :: DescribeDataSources

-- | <i>See:</i> <a>describeDataSources</a> smart constructor.
data DescribeDataSources

-- | The equal to operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
ddsEQ :: Lens' DescribeDataSources (Maybe Text)

-- | The greater than or equal to operator. The <tt>DataSource</tt> results
--   will have <tt>FilterVariable</tt> values that are greater than or
--   equal to the value specified with <tt>GE</tt> .
ddsGE :: Lens' DescribeDataSources (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, a <tt>DataSource</tt>
--   could have the <tt>Name</tt> <tt>2014-09-09-HolidayGiftMailer</tt> .
--   To search for this <tt>DataSource</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
ddsPrefix :: Lens' DescribeDataSources (Maybe Text)

-- | The greater than operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
ddsGT :: Lens' DescribeDataSources (Maybe Text)

-- | The not equal to operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values not equal to the value specified with
--   <tt>NE</tt> .
ddsNE :: Lens' DescribeDataSources (Maybe Text)

-- | The ID of the page in the paginated results.
ddsNextToken :: Lens' DescribeDataSources (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>DataSource</tt> . * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
ddsSortOrder :: Lens' DescribeDataSources (Maybe SortOrder)

-- | The maximum number of <tt>DataSource</tt> to include in the result.
ddsLimit :: Lens' DescribeDataSources (Maybe Natural)

-- | The less than operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
ddsLT :: Lens' DescribeDataSources (Maybe Text)

-- | Use one of the following variables to filter a list of
--   <tt>DataSource</tt> : * <tt>CreatedAt</tt> - Sets the search criteria
--   to <tt>DataSource</tt> creation dates. * <tt>Status</tt> - Sets the
--   search criteria to <tt>DataSource</tt> statuses. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>DataSource</tt> ____
--   <tt>Name</tt> . * <tt>DataUri</tt> - Sets the search criteria to the
--   URI of data files used to create the <tt>DataSource</tt> . The URI can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory. * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>DataSource</tt> creation.
ddsFilterVariable :: Lens' DescribeDataSources (Maybe DataSourceFilterVariable)

-- | The less than or equal to operator. The <tt>DataSource</tt> results
--   will have <tt>FilterVariable</tt> values that are less than or equal
--   to the value specified with <tt>LE</tt> .
ddsLE :: Lens' DescribeDataSources (Maybe Text)

-- | Creates a value of <a>DescribeDataSourcesResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddssrsResults</a> - A list of <tt>DataSource</tt> that meet the
--   search criteria.</li>
--   <li><a>ddssrsNextToken</a> - An ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>ddssrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeDataSourcesResponse :: Int -> DescribeDataSourcesResponse

-- | Represents the query results from a <a>DescribeDataSources</a>
--   operation. The content is essentially a list of <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>describeDataSourcesResponse</a> smart constructor.
data DescribeDataSourcesResponse

-- | A list of <tt>DataSource</tt> that meet the search criteria.
ddssrsResults :: Lens' DescribeDataSourcesResponse [DataSource]

-- | An ID of the next page in the paginated results that indicates at
--   least one more page follows.
ddssrsNextToken :: Lens' DescribeDataSourcesResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
ddssrsResponseStatus :: Lens' DescribeDataSourcesResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Data.Data Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources


-- | Returns a list of <tt>BatchPrediction</tt> operations that match the
--   search criteria in the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeBatchPredictions

-- | Creates a value of <a>DescribeBatchPredictions</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbpEQ</a> - The equal to operator. The <tt>BatchPrediction</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>dbpGE</a> - The greater than or equal to operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are greater than or equal to the value specified with
--   <tt>GE</tt> .</li>
--   <li><a>dbpPrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, a
--   <tt>Batch Prediction</tt> operation could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>BatchPrediction</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>dbpGT</a> - The greater than operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are greater than the value specified with <tt>GT</tt>
--   .</li>
--   <li><a>dbpNE</a> - The not equal to operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values not equal to the value specified with <tt>NE</tt> .</li>
--   <li><a>dbpNextToken</a> - An ID of the page in the paginated
--   results.</li>
--   <li><a>dbpSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>MLModel</tt> s. * <tt>asc</tt> -
--   Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>dbpLimit</a> - The number of pages of information to include in
--   the result. The range of acceptable values is <tt>1</tt> through
--   <tt>100</tt> . The default value is <tt>100</tt> .</li>
--   <li><a>dbpLT</a> - The less than operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are less than the value specified with <tt>LT</tt> .</li>
--   <li><a>dbpFilterVariable</a> - Use one of the following variables to
--   filter a list of <tt>BatchPrediction</tt> : * <tt>CreatedAt</tt> -
--   Sets the search criteria to the <tt>BatchPrediction</tt> creation
--   date. * <tt>Status</tt> - Sets the search criteria to the
--   <tt>BatchPrediction</tt> status. * <tt>Name</tt> - Sets the search
--   criteria to the contents of the <tt>BatchPrediction</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage
--   Solution (Amazon S3) bucket or directory.</li>
--   <li><a>dbpLE</a> - The less than or equal to operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are less than or equal to the value specified with
--   <tt>LE</tt> .</li>
--   </ul>
describeBatchPredictions :: DescribeBatchPredictions

-- | <i>See:</i> <a>describeBatchPredictions</a> smart constructor.
data DescribeBatchPredictions

-- | The equal to operator. The <tt>BatchPrediction</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
dbpEQ :: Lens' DescribeBatchPredictions (Maybe Text)

-- | The greater than or equal to operator. The <tt>BatchPrediction</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   or equal to the value specified with <tt>GE</tt> .
dbpGE :: Lens' DescribeBatchPredictions (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, a <tt>Batch
--   Prediction</tt> operation could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>BatchPrediction</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
dbpPrefix :: Lens' DescribeBatchPredictions (Maybe Text)

-- | The greater than operator. The <tt>BatchPrediction</tt> results will
--   have <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
dbpGT :: Lens' DescribeBatchPredictions (Maybe Text)

-- | The not equal to operator. The <tt>BatchPrediction</tt> results will
--   have <tt>FilterVariable</tt> values not equal to the value specified
--   with <tt>NE</tt> .
dbpNE :: Lens' DescribeBatchPredictions (Maybe Text)

-- | An ID of the page in the paginated results.
dbpNextToken :: Lens' DescribeBatchPredictions (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>MLModel</tt> s. * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
dbpSortOrder :: Lens' DescribeBatchPredictions (Maybe SortOrder)

-- | The number of pages of information to include in the result. The range
--   of acceptable values is <tt>1</tt> through <tt>100</tt> . The default
--   value is <tt>100</tt> .
dbpLimit :: Lens' DescribeBatchPredictions (Maybe Natural)

-- | The less than operator. The <tt>BatchPrediction</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
dbpLT :: Lens' DescribeBatchPredictions (Maybe Text)

-- | Use one of the following variables to filter a list of
--   <tt>BatchPrediction</tt> : * <tt>CreatedAt</tt> - Sets the search
--   criteria to the <tt>BatchPrediction</tt> creation date. *
--   <tt>Status</tt> - Sets the search criteria to the
--   <tt>BatchPrediction</tt> status. * <tt>Name</tt> - Sets the search
--   criteria to the contents of the <tt>BatchPrediction</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage
--   Solution (Amazon S3) bucket or directory.
dbpFilterVariable :: Lens' DescribeBatchPredictions (Maybe BatchPredictionFilterVariable)

-- | The less than or equal to operator. The <tt>BatchPrediction</tt>
--   results will have <tt>FilterVariable</tt> values that are less than or
--   equal to the value specified with <tt>LE</tt> .
dbpLE :: Lens' DescribeBatchPredictions (Maybe Text)

-- | Creates a value of <a>DescribeBatchPredictionsResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbpsrsResults</a> - A list of <tt>BatchPrediction</tt> objects
--   that meet the search criteria.</li>
--   <li><a>dbpsrsNextToken</a> - The ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>dbpsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeBatchPredictionsResponse :: Int -> DescribeBatchPredictionsResponse

-- | Represents the output of a <tt>DescribeBatchPredictions</tt>
--   operation. The content is essentially a list of
--   <tt>BatchPrediction</tt> s.
--   
--   <i>See:</i> <a>describeBatchPredictionsResponse</a> smart constructor.
data DescribeBatchPredictionsResponse

-- | A list of <tt>BatchPrediction</tt> objects that meet the search
--   criteria.
dbpsrsResults :: Lens' DescribeBatchPredictionsResponse [BatchPrediction]

-- | The ID of the next page in the paginated results that indicates at
--   least one more page follows.
dbpsrsNextToken :: Lens' DescribeBatchPredictionsResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dbpsrsResponseStatus :: Lens' DescribeBatchPredictionsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Data.Data Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions


-- | Deletes the specified tags associated with an ML object. After this
--   operation is complete, you can't recover deleted tags.
--   
--   If you specify a tag that doesn't exist, Amazon ML ignores it.
module Network.AWS.MachineLearning.DeleteTags

-- | Creates a value of <a>DeleteTags</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dTagKeys</a> - One or more tags to delete.</li>
--   <li><a>dResourceId</a> - The ID of the tagged ML object. For example,
--   <tt>exampleModelId</tt> .</li>
--   <li><a>dResourceType</a> - The type of the tagged ML object.</li>
--   </ul>
deleteTags :: Text -> TaggableResourceType -> DeleteTags

-- | <i>See:</i> <a>deleteTags</a> smart constructor.
data DeleteTags

-- | One or more tags to delete.
dTagKeys :: Lens' DeleteTags [Text]

-- | The ID of the tagged ML object. For example, <tt>exampleModelId</tt> .
dResourceId :: Lens' DeleteTags Text

-- | The type of the tagged ML object.
dResourceType :: Lens' DeleteTags TaggableResourceType

-- | Creates a value of <a>DeleteTagsResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>drsResourceId</a> - The ID of the ML object from which tags
--   were deleted.</li>
--   <li><a>drsResourceType</a> - The type of the ML object from which tags
--   were deleted.</li>
--   <li><a>drsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteTagsResponse :: Int -> DeleteTagsResponse

-- | Amazon ML returns the following elements.
--   
--   <i>See:</i> <a>deleteTagsResponse</a> smart constructor.
data DeleteTagsResponse

-- | The ID of the ML object from which tags were deleted.
drsResourceId :: Lens' DeleteTagsResponse (Maybe Text)

-- | The type of the ML object from which tags were deleted.
drsResourceType :: Lens' DeleteTagsResponse (Maybe TaggableResourceType)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
drsResponseStatus :: Lens' DeleteTagsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Data.Data.Data Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteTags.DeleteTags


-- | Deletes a real time endpoint of an <tt>MLModel</tt> .
module Network.AWS.MachineLearning.DeleteRealtimeEndpoint

-- | Creates a value of <a>DeleteRealtimeEndpoint</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dreMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation.</li>
--   </ul>
deleteRealtimeEndpoint :: Text -> DeleteRealtimeEndpoint

-- | <i>See:</i> <a>deleteRealtimeEndpoint</a> smart constructor.
data DeleteRealtimeEndpoint

-- | The ID assigned to the <tt>MLModel</tt> during creation.
dreMLModelId :: Lens' DeleteRealtimeEndpoint Text

-- | Creates a value of <a>DeleteRealtimeEndpointResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>drersRealtimeEndpointInfo</a> - The endpoint information of the
--   <tt>MLModel</tt></li>
--   <li><a>drersMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelId</tt> in the request.</li>
--   <li><a>drersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteRealtimeEndpointResponse :: Int -> DeleteRealtimeEndpointResponse

-- | Represents the output of an <tt>DeleteRealtimeEndpoint</tt> operation.
--   
--   The result contains the <tt>MLModelId</tt> and the endpoint
--   information for the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>deleteRealtimeEndpointResponse</a> smart constructor.
data DeleteRealtimeEndpointResponse

-- | The endpoint information of the <tt>MLModel</tt>
drersRealtimeEndpointInfo :: Lens' DeleteRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelId</tt>
--   in the request.
drersMLModelId :: Lens' DeleteRealtimeEndpointResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
drersResponseStatus :: Lens' DeleteRealtimeEndpointResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Data.Data.Data Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint


-- | Assigns the <tt>DELETED</tt> status to an <tt>MLModel</tt> , rendering
--   it unusable.
--   
--   After using the <tt>DeleteMLModel</tt> operation, you can use the
--   <tt>GetMLModel</tt> operation to verify that the status of the
--   <tt>MLModel</tt> changed to DELETED.
--   
--   <b>Caution:</b> The result of the <tt>DeleteMLModel</tt> operation is
--   irreversible.
module Network.AWS.MachineLearning.DeleteMLModel

-- | Creates a value of <a>DeleteMLModel</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmMLModelId</a> - A user-supplied ID that uniquely identifies
--   the <tt>MLModel</tt> .</li>
--   </ul>
deleteMLModel :: Text -> DeleteMLModel

-- | <i>See:</i> <a>deleteMLModel</a> smart constructor.
data DeleteMLModel

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
dmlmMLModelId :: Lens' DeleteMLModel Text

-- | Creates a value of <a>DeleteMLModelResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmrsMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelID</tt> in the request.</li>
--   <li><a>dmlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteMLModelResponse :: Int -> DeleteMLModelResponse

-- | Represents the output of a <tt>DeleteMLModel</tt> operation.
--   
--   You can use the <tt>GetMLModel</tt> operation and check the value of
--   the <tt>Status</tt> parameter to see whether an <tt>MLModel</tt> is
--   marked as <tt>DELETED</tt> .
--   
--   <i>See:</i> <a>deleteMLModelResponse</a> smart constructor.
data DeleteMLModelResponse

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelID</tt>
--   in the request.
dmlmrsMLModelId :: Lens' DeleteMLModelResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dmlmrsResponseStatus :: Lens' DeleteMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Data.Data.Data Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel


-- | Assigns the <tt>DELETED</tt> status to an <tt>Evaluation</tt> ,
--   rendering it unusable.
--   
--   After invoking the <tt>DeleteEvaluation</tt> operation, you can use
--   the <tt>GetEvaluation</tt> operation to verify that the status of the
--   <tt>Evaluation</tt> changed to <tt>DELETED</tt> .
--   
--   _<b>_Caution</b> The results of the <tt>DeleteEvaluation</tt>
--   operation are irreversible.
--   
--   __
module Network.AWS.MachineLearning.DeleteEvaluation

-- | Creates a value of <a>DeleteEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>deEvaluationId</a> - A user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> to delete.</li>
--   </ul>
deleteEvaluation :: Text -> DeleteEvaluation

-- | <i>See:</i> <a>deleteEvaluation</a> smart constructor.
data DeleteEvaluation

-- | A user-supplied ID that uniquely identifies the <tt>Evaluation</tt> to
--   delete.
deEvaluationId :: Lens' DeleteEvaluation Text

-- | Creates a value of <a>DeleteEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dersEvaluationId</a> - A user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> . This value should be identical to
--   the value of the <tt>EvaluationId</tt> in the request.</li>
--   <li><a>dersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteEvaluationResponse :: Int -> DeleteEvaluationResponse

-- | Represents the output of a <tt>DeleteEvaluation</tt> operation. The
--   output indicates that Amazon Machine Learning (Amazon ML) received the
--   request.
--   
--   You can use the <tt>GetEvaluation</tt> operation and check the value
--   of the <tt>Status</tt> parameter to see whether an <tt>Evaluation</tt>
--   is marked as <tt>DELETED</tt> .
--   
--   <i>See:</i> <a>deleteEvaluationResponse</a> smart constructor.
data DeleteEvaluationResponse

-- | A user-supplied ID that uniquely identifies the <tt>Evaluation</tt> .
--   This value should be identical to the value of the
--   <tt>EvaluationId</tt> in the request.
dersEvaluationId :: Lens' DeleteEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dersResponseStatus :: Lens' DeleteEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation


-- | Assigns the DELETED status to a <tt>DataSource</tt> , rendering it
--   unusable.
--   
--   After using the <tt>DeleteDataSource</tt> operation, you can use the
--   <tt>GetDataSource</tt> operation to verify that the status of the
--   <tt>DataSource</tt> changed to DELETED.
--   
--   <b>Caution:</b> The results of the <tt>DeleteDataSource</tt> operation
--   are irreversible.
module Network.AWS.MachineLearning.DeleteDataSource

-- | Creates a value of <a>DeleteDataSource</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> .</li>
--   </ul>
deleteDataSource :: Text -> DeleteDataSource

-- | <i>See:</i> <a>deleteDataSource</a> smart constructor.
data DeleteDataSource

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
ddsDataSourceId :: Lens' DeleteDataSource Text

-- | Creates a value of <a>DeleteDataSourceResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddsrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> . This value should be identical to
--   the value of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>ddsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteDataSourceResponse :: Int -> DeleteDataSourceResponse

-- | Represents the output of a <tt>DeleteDataSource</tt> operation.
--   
--   <i>See:</i> <a>deleteDataSourceResponse</a> smart constructor.
data DeleteDataSourceResponse

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
--   This value should be identical to the value of the
--   <tt>DataSourceID</tt> in the request.
ddsrsDataSourceId :: Lens' DeleteDataSourceResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
ddsrsResponseStatus :: Lens' DeleteDataSourceResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Data.Data.Data Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource


-- | Assigns the DELETED status to a <tt>BatchPrediction</tt> , rendering
--   it unusable.
--   
--   After using the <tt>DeleteBatchPrediction</tt> operation, you can use
--   the <tt>GetBatchPrediction</tt> operation to verify that the status of
--   the <tt>BatchPrediction</tt> changed to DELETED.
--   
--   <b>Caution:</b> The result of the <tt>DeleteBatchPrediction</tt>
--   operation is irreversible.
module Network.AWS.MachineLearning.DeleteBatchPrediction

-- | Creates a value of <a>DeleteBatchPrediction</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbpBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> .</li>
--   </ul>
deleteBatchPrediction :: Text -> DeleteBatchPrediction

-- | <i>See:</i> <a>deleteBatchPrediction</a> smart constructor.
data DeleteBatchPrediction

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> .
dbpBatchPredictionId :: Lens' DeleteBatchPrediction Text

-- | Creates a value of <a>DeleteBatchPredictionResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbprsBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> . This value should be
--   identical to the value of the <tt>BatchPredictionID</tt> in the
--   request.</li>
--   <li><a>dbprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteBatchPredictionResponse :: Int -> DeleteBatchPredictionResponse

-- | Represents the output of a <tt>DeleteBatchPrediction</tt> operation.
--   
--   You can use the <tt>GetBatchPrediction</tt> operation and check the
--   value of the <tt>Status</tt> parameter to see whether a
--   <tt>BatchPrediction</tt> is marked as <tt>DELETED</tt> .
--   
--   <i>See:</i> <a>deleteBatchPredictionResponse</a> smart constructor.
data DeleteBatchPredictionResponse

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> . This value should be identical to the value
--   of the <tt>BatchPredictionID</tt> in the request.
dbprsBatchPredictionId :: Lens' DeleteBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dbprsResponseStatus :: Lens' DeleteBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction


-- | Creates a real-time endpoint for the <tt>MLModel</tt> . The endpoint
--   contains the URI of the <tt>MLModel</tt> ; that is, the location to
--   send real-time prediction requests for the specified <tt>MLModel</tt>
--   .
module Network.AWS.MachineLearning.CreateRealtimeEndpoint

-- | Creates a value of <a>CreateRealtimeEndpoint</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>creMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation.</li>
--   </ul>
createRealtimeEndpoint :: Text -> CreateRealtimeEndpoint

-- | <i>See:</i> <a>createRealtimeEndpoint</a> smart constructor.
data CreateRealtimeEndpoint

-- | The ID assigned to the <tt>MLModel</tt> during creation.
creMLModelId :: Lens' CreateRealtimeEndpoint Text

-- | Creates a value of <a>CreateRealtimeEndpointResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>crersRealtimeEndpointInfo</a> - The endpoint information of the
--   <tt>MLModel</tt></li>
--   <li><a>crersMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelId</tt> in the request.</li>
--   <li><a>crersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createRealtimeEndpointResponse :: Int -> CreateRealtimeEndpointResponse

-- | Represents the output of an <tt>CreateRealtimeEndpoint</tt> operation.
--   
--   The result contains the <tt>MLModelId</tt> and the endpoint
--   information for the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>createRealtimeEndpointResponse</a> smart constructor.
data CreateRealtimeEndpointResponse

-- | The endpoint information of the <tt>MLModel</tt>
crersRealtimeEndpointInfo :: Lens' CreateRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelId</tt>
--   in the request.
crersMLModelId :: Lens' CreateRealtimeEndpointResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
crersResponseStatus :: Lens' CreateRealtimeEndpointResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Data.Data.Data Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Show.Show Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Read.Read Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint


-- | Creates a new <tt>MLModel</tt> using the <tt>DataSource</tt> and the
--   recipe as information sources.
--   
--   An <tt>MLModel</tt> is nearly immutable. Users can update only the
--   <tt>MLModelName</tt> and the <tt>ScoreThreshold</tt> in an
--   <tt>MLModel</tt> without creating a new <tt>MLModel</tt> .
--   
--   <tt>CreateMLModel</tt> is an asynchronous operation. In response to
--   <tt>CreateMLModel</tt> , Amazon Machine Learning (Amazon ML)
--   immediately returns and sets the <tt>MLModel</tt> status to
--   <tt>PENDING</tt> . After the <tt>MLModel</tt> has been created and
--   ready is for use, Amazon ML sets the status to <tt>COMPLETED</tt> .
--   
--   You can use the <tt>GetMLModel</tt> operation to check the progress of
--   the <tt>MLModel</tt> during the creation operation.
--   
--   <tt>CreateMLModel</tt> requires a <tt>DataSource</tt> with computed
--   statistics, which can be created by setting <tt>ComputeStatistics</tt>
--   to <tt>true</tt> in <tt>CreateDataSourceFromRDS</tt> ,
--   <tt>CreateDataSourceFromS3</tt> , or
--   <tt>CreateDataSourceFromRedshift</tt> operations.
module Network.AWS.MachineLearning.CreateMLModel

-- | Creates a value of <a>CreateMLModel</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cmlmRecipe</a> - The data recipe for creating the
--   <tt>MLModel</tt> . You must specify either the recipe or its URI. If
--   you don't specify a recipe or its URI, Amazon ML creates a
--   default.</li>
--   <li><a>cmlmRecipeURI</a> - The Amazon Simple Storage Service (Amazon
--   S3) location and file name that contains the <tt>MLModel</tt> recipe.
--   You must specify either the recipe or its URI. If you don't specify a
--   recipe or its URI, Amazon ML creates a default.</li>
--   <li><a>cmlmMLModelName</a> - A user-supplied name or description of
--   the <tt>MLModel</tt> .</li>
--   <li><a>cmlmParameters</a> - A list of the training parameters in the
--   <tt>MLModel</tt> . The list is implemented as a map of key-value
--   pairs. The following is the current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>cmlmMLModelId</a> - A user-supplied ID that uniquely identifies
--   the <tt>MLModel</tt> .</li>
--   <li><a>cmlmMLModelType</a> - The category of supervised learning that
--   this <tt>MLModel</tt> will address. Choose from the following types: *
--   Choose <tt>REGRESSION</tt> if the <tt>MLModel</tt> will be used to
--   predict a numeric value. * Choose <tt>BINARY</tt> if the
--   <tt>MLModel</tt> result has two possible values. * Choose
--   <tt>MULTICLASS</tt> if the <tt>MLModel</tt> result has a limited
--   number of values. For more information, see the <a>Amazon Machine
--   Learning Developer Guide</a> .</li>
--   <li><a>cmlmTrainingDataSourceId</a> - The <tt>DataSource</tt> that
--   points to the training data.</li>
--   </ul>
createMLModel :: Text -> MLModelType -> Text -> CreateMLModel

-- | <i>See:</i> <a>createMLModel</a> smart constructor.
data CreateMLModel

-- | The data recipe for creating the <tt>MLModel</tt> . You must specify
--   either the recipe or its URI. If you don't specify a recipe or its
--   URI, Amazon ML creates a default.
cmlmRecipe :: Lens' CreateMLModel (Maybe Text)

-- | The Amazon Simple Storage Service (Amazon S3) location and file name
--   that contains the <tt>MLModel</tt> recipe. You must specify either the
--   recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
--   creates a default.
cmlmRecipeURI :: Lens' CreateMLModel (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
cmlmMLModelName :: Lens' CreateMLModel (Maybe Text)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
cmlmParameters :: Lens' CreateMLModel (HashMap Text Text)

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
cmlmMLModelId :: Lens' CreateMLModel Text

-- | The category of supervised learning that this <tt>MLModel</tt> will
--   address. Choose from the following types: * Choose <tt>REGRESSION</tt>
--   if the <tt>MLModel</tt> will be used to predict a numeric value. *
--   Choose <tt>BINARY</tt> if the <tt>MLModel</tt> result has two possible
--   values. * Choose <tt>MULTICLASS</tt> if the <tt>MLModel</tt> result
--   has a limited number of values. For more information, see the
--   <a>Amazon Machine Learning Developer Guide</a> .
cmlmMLModelType :: Lens' CreateMLModel MLModelType

-- | The <tt>DataSource</tt> that points to the training data.
cmlmTrainingDataSourceId :: Lens' CreateMLModel Text

-- | Creates a value of <a>CreateMLModelResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cmlmrsMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelId</tt> in the request.</li>
--   <li><a>cmlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createMLModelResponse :: Int -> CreateMLModelResponse

-- | Represents the output of a <tt>CreateMLModel</tt> operation, and is an
--   acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateMLModel</tt> operation is asynchronous. You can poll for
--   status updates by using the <tt>GetMLModel</tt> operation and checking
--   the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createMLModelResponse</a> smart constructor.
data CreateMLModelResponse

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelId</tt>
--   in the request.
cmlmrsMLModelId :: Lens' CreateMLModelResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cmlmrsResponseStatus :: Lens' CreateMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Data.Data.Data Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateMLModel.CreateMLModel


-- | Creates a new <tt>Evaluation</tt> of an <tt>MLModel</tt> . An
--   <tt>MLModel</tt> is evaluated on a set of observations associated to a
--   <tt>DataSource</tt> . Like a <tt>DataSource</tt> for an
--   <tt>MLModel</tt> , the <tt>DataSource</tt> for an <tt>Evaluation</tt>
--   contains values for the <tt>Target Variable</tt> . The
--   <tt>Evaluation</tt> compares the predicted result for each observation
--   to the actual outcome and provides a summary so that you know how
--   effective the <tt>MLModel</tt> functions on the test data. Evaluation
--   generates a relevant performance metric, such as BinaryAUC,
--   RegressionRMSE or MulticlassAvgFScore based on the corresponding
--   <tt>MLModelType</tt> : <tt>BINARY</tt> , <tt>REGRESSION</tt> or
--   <tt>MULTICLASS</tt> .
--   
--   <tt>CreateEvaluation</tt> is an asynchronous operation. In response to
--   <tt>CreateEvaluation</tt> , Amazon Machine Learning (Amazon ML)
--   immediately returns and sets the evaluation status to <tt>PENDING</tt>
--   . After the <tt>Evaluation</tt> is created and ready for use, Amazon
--   ML sets the status to <tt>COMPLETED</tt> .
--   
--   You can use the <tt>GetEvaluation</tt> operation to check progress of
--   the evaluation during the creation operation.
module Network.AWS.MachineLearning.CreateEvaluation

-- | Creates a value of <a>CreateEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ceEvaluationName</a> - A user-supplied name or description of
--   the <tt>Evaluation</tt> .</li>
--   <li><a>ceEvaluationId</a> - A user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> .</li>
--   <li><a>ceMLModelId</a> - The ID of the <tt>MLModel</tt> to evaluate.
--   The schema used in creating the <tt>MLModel</tt> must match the schema
--   of the <tt>DataSource</tt> used in the <tt>Evaluation</tt> .</li>
--   <li><a>ceEvaluationDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> for the evaluation. The schema of the
--   <tt>DataSource</tt> must match the schema used to create the
--   <tt>MLModel</tt> .</li>
--   </ul>
createEvaluation :: Text -> Text -> Text -> CreateEvaluation

-- | <i>See:</i> <a>createEvaluation</a> smart constructor.
data CreateEvaluation

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
ceEvaluationName :: Lens' CreateEvaluation (Maybe Text)

-- | A user-supplied ID that uniquely identifies the <tt>Evaluation</tt> .
ceEvaluationId :: Lens' CreateEvaluation Text

-- | The ID of the <tt>MLModel</tt> to evaluate. The schema used in
--   creating the <tt>MLModel</tt> must match the schema of the
--   <tt>DataSource</tt> used in the <tt>Evaluation</tt> .
ceMLModelId :: Lens' CreateEvaluation Text

-- | The ID of the <tt>DataSource</tt> for the evaluation. The schema of
--   the <tt>DataSource</tt> must match the schema used to create the
--   <tt>MLModel</tt> .
ceEvaluationDataSourceId :: Lens' CreateEvaluation Text

-- | Creates a value of <a>CreateEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cersEvaluationId</a> - The user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> . This value should be identical to
--   the value of the <tt>EvaluationId</tt> in the request.</li>
--   <li><a>cersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createEvaluationResponse :: Int -> CreateEvaluationResponse

-- | Represents the output of a <tt>CreateEvaluation</tt> operation, and is
--   an acknowledgement that Amazon ML received the request.
--   
--   <tt>CreateEvaluation</tt> operation is asynchronous. You can poll for
--   status updates by using the <tt>GetEvcaluation</tt> operation and
--   checking the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createEvaluationResponse</a> smart constructor.
data CreateEvaluationResponse

-- | The user-supplied ID that uniquely identifies the <tt>Evaluation</tt>
--   . This value should be identical to the value of the
--   <tt>EvaluationId</tt> in the request.
cersEvaluationId :: Lens' CreateEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cersResponseStatus :: Lens' CreateEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation


-- | Creates a <tt>DataSource</tt> object. A <tt>DataSource</tt> references
--   data that can be used to perform <tt>CreateMLModel</tt> ,
--   <tt>CreateEvaluation</tt> , or <tt>CreateBatchPrediction</tt>
--   operations.
--   
--   <tt>CreateDataSourceFromS3</tt> is an asynchronous operation. In
--   response to <tt>CreateDataSourceFromS3</tt> , Amazon Machine Learning
--   (Amazon ML) immediately returns and sets the <tt>DataSource</tt>
--   status to <tt>PENDING</tt> . After the <tt>DataSource</tt> has been
--   created and is ready for use, Amazon ML sets the <tt>Status</tt>
--   parameter to <tt>COMPLETED</tt> . <tt>DataSource</tt> in the
--   <tt>COMPLETED</tt> or <tt>PENDING</tt> state can be used to perform
--   only <tt>CreateMLModel</tt> , <tt>CreateEvaluation</tt> or
--   <tt>CreateBatchPrediction</tt> operations.
--   
--   If Amazon ML can't accept the input source, it sets the
--   <tt>Status</tt> parameter to <tt>FAILED</tt> and includes an error
--   message in the <tt>Message</tt> attribute of the
--   <tt>GetDataSource</tt> operation response.
--   
--   The observation data used in a <tt>DataSource</tt> should be ready to
--   use; that is, it should have a consistent structure, and missing data
--   values should be kept to a minimum. The observation data must reside
--   in one or more .csv files in an Amazon Simple Storage Service (Amazon
--   S3) location, along with a schema that describes the data items by
--   name and type. The same schema must be used for all of the data files
--   referenced by the <tt>DataSource</tt> .
--   
--   After the <tt>DataSource</tt> has been created, it's ready to use in
--   evaluations and batch predictions. If you plan to use the
--   <tt>DataSource</tt> to train an <tt>MLModel</tt> , the
--   <tt>DataSource</tt> also needs a recipe. A recipe describes how each
--   input variable will be used in training an <tt>MLModel</tt> . Will the
--   variable be included or excluded from training? Will the variable be
--   manipulated; for example, will it be combined with another variable or
--   will it be split apart into word combinations? The recipe provides
--   answers to these questions.
module Network.AWS.MachineLearning.CreateDataSourceFromS3

-- | Creates a value of <a>CreateDataSourceFromS3</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfsDataSourceName</a> - A user-supplied name or description
--   of the <tt>DataSource</tt> .</li>
--   <li><a>cdsfsComputeStatistics</a> - The compute statistics for a
--   <tt>DataSource</tt> . The statistics are generated from the
--   observation data referenced by a <tt>DataSource</tt> . Amazon ML uses
--   the statistics internally during <tt>MLModel</tt> training. This
--   parameter must be set to <tt>true</tt> if the DataSourceneeds to be
--   used for <tt>MLModel</tt> training.</li>
--   <li><a>cdsfsDataSourceId</a> - A user-supplied identifier that
--   uniquely identifies the <tt>DataSource</tt> .</li>
--   <li><a>cdsfsDataSpec</a> - The data specification of a
--   <tt>DataSource</tt> : * DataLocationS3 - The Amazon S3 location of the
--   observation data. * DataSchemaLocationS3 - The Amazon S3 location of
--   the <tt>DataSchema</tt> . * DataSchema - A JSON string representing
--   the schema. This is not required if <tt>DataSchemaUri</tt> is
--   specified. * DataRearrangement - A JSON string that represents the
--   splitting and rearrangement requirements for the <tt>Datasource</tt> .
--   Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt></li>
--   </ul>
createDataSourceFromS3 :: Text -> S3DataSpec -> CreateDataSourceFromS3

-- | <i>See:</i> <a>createDataSourceFromS3</a> smart constructor.
data CreateDataSourceFromS3

-- | A user-supplied name or description of the <tt>DataSource</tt> .
cdsfsDataSourceName :: Lens' CreateDataSourceFromS3 (Maybe Text)

-- | The compute statistics for a <tt>DataSource</tt> . The statistics are
--   generated from the observation data referenced by a
--   <tt>DataSource</tt> . Amazon ML uses the statistics internally during
--   <tt>MLModel</tt> training. This parameter must be set to <tt>true</tt>
--   if the DataSourceneeds to be used for <tt>MLModel</tt> training.
cdsfsComputeStatistics :: Lens' CreateDataSourceFromS3 (Maybe Bool)

-- | A user-supplied identifier that uniquely identifies the
--   <tt>DataSource</tt> .
cdsfsDataSourceId :: Lens' CreateDataSourceFromS3 Text

-- | The data specification of a <tt>DataSource</tt> : * DataLocationS3 -
--   The Amazon S3 location of the observation data. * DataSchemaLocationS3
--   - The Amazon S3 location of the <tt>DataSchema</tt> . * DataSchema - A
--   JSON string representing the schema. This is not required if
--   <tt>DataSchemaUri</tt> is specified. * DataRearrangement - A JSON
--   string that represents the splitting and rearrangement requirements
--   for the <tt>Datasource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt>
cdsfsDataSpec :: Lens' CreateDataSourceFromS3 S3DataSpec

-- | Creates a value of <a>CreateDataSourceFromS3Response</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfsrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> . This value should be identical to
--   the value of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>cdsfsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createDataSourceFromS3Response :: Int -> CreateDataSourceFromS3Response

-- | Represents the output of a <tt>CreateDataSourceFromS3</tt> operation,
--   and is an acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateDataSourceFromS3</tt> operation is asynchronous. You can
--   poll for updates by using the <tt>GetBatchPrediction</tt> operation
--   and checking the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createDataSourceFromS3Response</a> smart constructor.
data CreateDataSourceFromS3Response

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
--   This value should be identical to the value of the
--   <tt>DataSourceID</tt> in the request.
cdsfsrsDataSourceId :: Lens' CreateDataSourceFromS3Response (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cdsfsrsResponseStatus :: Lens' CreateDataSourceFromS3Response Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3


-- | Creates a <tt>DataSource</tt> from a database hosted on an Amazon
--   Redshift cluster. A <tt>DataSource</tt> references data that can be
--   used to perform either <tt>CreateMLModel</tt> ,
--   <tt>CreateEvaluation</tt> , or <tt>CreateBatchPrediction</tt>
--   operations.
--   
--   <tt>CreateDataSourceFromRedshift</tt> is an asynchronous operation. In
--   response to <tt>CreateDataSourceFromRedshift</tt> , Amazon Machine
--   Learning (Amazon ML) immediately returns and sets the
--   <tt>DataSource</tt> status to <tt>PENDING</tt> . After the
--   <tt>DataSource</tt> is created and ready for use, Amazon ML sets the
--   <tt>Status</tt> parameter to <tt>COMPLETED</tt> . <tt>DataSource</tt>
--   in <tt>COMPLETED</tt> or <tt>PENDING</tt> states can be used to
--   perform only <tt>CreateMLModel</tt> , <tt>CreateEvaluation</tt> , or
--   <tt>CreateBatchPrediction</tt> operations.
--   
--   If Amazon ML can't accept the input source, it sets the
--   <tt>Status</tt> parameter to <tt>FAILED</tt> and includes an error
--   message in the <tt>Message</tt> attribute of the
--   <tt>GetDataSource</tt> operation response.
--   
--   The observations should be contained in the database hosted on an
--   Amazon Redshift cluster and should be specified by a
--   <tt>SelectSqlQuery</tt> query. Amazon ML executes an <tt>Unload</tt>
--   command in Amazon Redshift to transfer the result set of the
--   <tt>SelectSqlQuery</tt> query to <tt>S3StagingLocation</tt> .
--   
--   After the <tt>DataSource</tt> has been created, it's ready for use in
--   evaluations and batch predictions. If you plan to use the
--   <tt>DataSource</tt> to train an <tt>MLModel</tt> , the
--   <tt>DataSource</tt> also requires a recipe. A recipe describes how
--   each input variable will be used in training an <tt>MLModel</tt> .
--   Will the variable be included or excluded from training? Will the
--   variable be manipulated; for example, will it be combined with another
--   variable or will it be split apart into word combinations? The recipe
--   provides answers to these questions.
--   
--   You can't change an existing datasource, but you can copy and modify
--   the settings from an existing Amazon Redshift datasource to create a
--   new datasource. To do so, call <tt>GetDataSource</tt> for an existing
--   datasource and copy the values to a <tt>CreateDataSource</tt> call.
--   Change the settings that you want to change and make sure that all
--   required fields have the appropriate values.
module Network.AWS.MachineLearning.CreateDataSourceFromRedshift

-- | Creates a value of <a>CreateDataSourceFromRedshift</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrDataSourceName</a> - A user-supplied name or description
--   of the <tt>DataSource</tt> .</li>
--   <li><a>cdsfrComputeStatistics</a> - The compute statistics for a
--   <tt>DataSource</tt> . The statistics are generated from the
--   observation data referenced by a <tt>DataSource</tt> . Amazon ML uses
--   the statistics internally during <tt>MLModel</tt> training. This
--   parameter must be set to <tt>true</tt> if the <tt>DataSource</tt>
--   needs to be used for <tt>MLModel</tt> training.</li>
--   <li><a>cdsfrDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> .</li>
--   <li><a>cdsfrDataSpec</a> - The data specification of an Amazon
--   Redshift <tt>DataSource</tt> : * DatabaseInformation - *
--   <tt>DatabaseName</tt> - The name of the Amazon Redshift database. *
--   <tt>ClusterIdentifier</tt> - The unique ID for the Amazon Redshift
--   cluster. * DatabaseCredentials - The AWS Identity and Access
--   Management (IAM) credentials that are used to connect to the Amazon
--   Redshift database. * SelectSqlQuery - The query that is used to
--   retrieve the observation data for the <tt>Datasource</tt> . *
--   S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
--   location for staging Amazon Redshift data. The data retrieved from
--   Amazon Redshift using the <tt>SelectSqlQuery</tt> query is stored in
--   this location. * DataSchemaUri - The Amazon S3 location of the
--   <tt>DataSchema</tt> . * DataSchema - A JSON string representing the
--   schema. This is not required if <tt>DataSchemaUri</tt> is specified. *
--   DataRearrangement - A JSON string that represents the splitting and
--   rearrangement requirements for the <tt>DataSource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt></li>
--   <li><a>cdsfrRoleARN</a> - A fully specified role Amazon Resource Name
--   (ARN). Amazon ML assumes the role on behalf of the user to create the
--   following: * A security group to allow Amazon ML to execute the
--   <tt>SelectSqlQuery</tt> query on an Amazon Redshift cluster * An
--   Amazon S3 bucket policy to grant Amazon ML read/write permissions on
--   the <tt>S3StagingLocation</tt></li>
--   </ul>
createDataSourceFromRedshift :: Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift

-- | <i>See:</i> <a>createDataSourceFromRedshift</a> smart constructor.
data CreateDataSourceFromRedshift

-- | A user-supplied name or description of the <tt>DataSource</tt> .
cdsfrDataSourceName :: Lens' CreateDataSourceFromRedshift (Maybe Text)

-- | The compute statistics for a <tt>DataSource</tt> . The statistics are
--   generated from the observation data referenced by a
--   <tt>DataSource</tt> . Amazon ML uses the statistics internally during
--   <tt>MLModel</tt> training. This parameter must be set to <tt>true</tt>
--   if the <tt>DataSource</tt> needs to be used for <tt>MLModel</tt>
--   training.
cdsfrComputeStatistics :: Lens' CreateDataSourceFromRedshift (Maybe Bool)

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
cdsfrDataSourceId :: Lens' CreateDataSourceFromRedshift Text

-- | The data specification of an Amazon Redshift <tt>DataSource</tt> : *
--   DatabaseInformation - * <tt>DatabaseName</tt> - The name of the Amazon
--   Redshift database. * <tt>ClusterIdentifier</tt> - The unique ID for
--   the Amazon Redshift cluster. * DatabaseCredentials - The AWS Identity
--   and Access Management (IAM) credentials that are used to connect to
--   the Amazon Redshift database. * SelectSqlQuery - The query that is
--   used to retrieve the observation data for the <tt>Datasource</tt> . *
--   S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
--   location for staging Amazon Redshift data. The data retrieved from
--   Amazon Redshift using the <tt>SelectSqlQuery</tt> query is stored in
--   this location. * DataSchemaUri - The Amazon S3 location of the
--   <tt>DataSchema</tt> . * DataSchema - A JSON string representing the
--   schema. This is not required if <tt>DataSchemaUri</tt> is specified. *
--   DataRearrangement - A JSON string that represents the splitting and
--   rearrangement requirements for the <tt>DataSource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt>
cdsfrDataSpec :: Lens' CreateDataSourceFromRedshift RedshiftDataSpec

-- | A fully specified role Amazon Resource Name (ARN). Amazon ML assumes
--   the role on behalf of the user to create the following: * A security
--   group to allow Amazon ML to execute the <tt>SelectSqlQuery</tt> query
--   on an Amazon Redshift cluster * An Amazon S3 bucket policy to grant
--   Amazon ML read/write permissions on the <tt>S3StagingLocation</tt>
cdsfrRoleARN :: Lens' CreateDataSourceFromRedshift Text

-- | Creates a value of <a>CreateDataSourceFromRedshiftResponse</a> with
--   the minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the datasource. This value should be identical to the value
--   of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>cdsfrrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createDataSourceFromRedshiftResponse :: Int -> CreateDataSourceFromRedshiftResponse

-- | Represents the output of a <tt>CreateDataSourceFromRedshift</tt>
--   operation, and is an acknowledgement that Amazon ML received the
--   request.
--   
--   The <tt>CreateDataSourceFromRedshift</tt> operation is asynchronous.
--   You can poll for updates by using the <tt>GetBatchPrediction</tt>
--   operation and checking the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createDataSourceFromRedshiftResponse</a> smart
--   constructor.
data CreateDataSourceFromRedshiftResponse

-- | A user-supplied ID that uniquely identifies the datasource. This value
--   should be identical to the value of the <tt>DataSourceID</tt> in the
--   request.
cdsfrrsDataSourceId :: Lens' CreateDataSourceFromRedshiftResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cdsfrrsResponseStatus :: Lens' CreateDataSourceFromRedshiftResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift


-- | Creates a <tt>DataSource</tt> object from an <a>Amazon Relational
--   Database Service</a> (Amazon RDS). A <tt>DataSource</tt> references
--   data that can be used to perform <tt>CreateMLModel</tt> ,
--   <tt>CreateEvaluation</tt> , or <tt>CreateBatchPrediction</tt>
--   operations.
--   
--   <tt>CreateDataSourceFromRDS</tt> is an asynchronous operation. In
--   response to <tt>CreateDataSourceFromRDS</tt> , Amazon Machine Learning
--   (Amazon ML) immediately returns and sets the <tt>DataSource</tt>
--   status to <tt>PENDING</tt> . After the <tt>DataSource</tt> is created
--   and ready for use, Amazon ML sets the <tt>Status</tt> parameter to
--   <tt>COMPLETED</tt> . <tt>DataSource</tt> in the <tt>COMPLETED</tt> or
--   <tt>PENDING</tt> state can be used only to perform
--   <tt>&gt;CreateMLModel</tt> &gt;, <tt>CreateEvaluation</tt> , or
--   <tt>CreateBatchPrediction</tt> operations.
--   
--   If Amazon ML cannot accept the input source, it sets the
--   <tt>Status</tt> parameter to <tt>FAILED</tt> and includes an error
--   message in the <tt>Message</tt> attribute of the
--   <tt>GetDataSource</tt> operation response.
module Network.AWS.MachineLearning.CreateDataSourceFromRDS

-- | Creates a value of <a>CreateDataSourceFromRDS</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrdsDataSourceName</a> - A user-supplied name or description
--   of the <tt>DataSource</tt> .</li>
--   <li><a>cdsfrdsComputeStatistics</a> - The compute statistics for a
--   <tt>DataSource</tt> . The statistics are generated from the
--   observation data referenced by a <tt>DataSource</tt> . Amazon ML uses
--   the statistics internally during <tt>MLModel</tt> training. This
--   parameter must be set to <tt>true</tt> if the DataSourceneeds to be
--   used for <tt>MLModel</tt> training.</li>
--   <li><a>cdsfrdsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> . Typically, an Amazon Resource
--   Number (ARN) becomes the ID for a <tt>DataSource</tt> .</li>
--   <li><a>cdsfrdsRDSData</a> - The data specification of an Amazon RDS
--   <tt>DataSource</tt> : * DatabaseInformation - * <tt>DatabaseName</tt>
--   - The name of the Amazon RDS database. * <tt>InstanceIdentifier </tt>
--   - A unique identifier for the Amazon RDS database instance. *
--   DatabaseCredentials - AWS Identity and Access Management (IAM)
--   credentials that are used to connect to the Amazon RDS database. *
--   ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an
--   EC2 instance to carry out the copy task from Amazon RDS to Amazon
--   Simple Storage Service (Amazon S3). For more information, see <a>Role
--   templates</a> for data pipelines. * ServiceRole - A role
--   (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to
--   monitor the progress of the copy task from Amazon RDS to Amazon S3.
--   For more information, see <a>Role templates</a> for data pipelines. *
--   SecurityInfo - The security information to use to access an RDS DB
--   instance. You need to set up appropriate ingress rules for the
--   security entity IDs provided to allow access to the Amazon RDS
--   instance. Specify a [<tt>SubnetId</tt> , <tt>SecurityGroupIds</tt> ]
--   pair for a VPC-based RDS DB instance. * SelectSqlQuery - A query that
--   is used to retrieve the observation data for the <tt>Datasource</tt> .
--   * S3StagingLocation - The Amazon S3 location for staging Amazon RDS
--   data. The data retrieved from Amazon RDS using <tt>SelectSqlQuery</tt>
--   is stored in this location. * DataSchemaUri - The Amazon S3 location
--   of the <tt>DataSchema</tt> . * DataSchema - A JSON string representing
--   the schema. This is not required if <tt>DataSchemaUri</tt> is
--   specified. * DataRearrangement - A JSON string that represents the
--   splitting and rearrangement requirements for the <tt>Datasource</tt> .
--   Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt></li>
--   <li><a>cdsfrdsRoleARN</a> - The role that Amazon ML assumes on behalf
--   of the user to create and activate a data pipeline in the user's
--   account and copy data using the <tt>SelectSqlQuery</tt> query from
--   Amazon RDS to Amazon S3.</li>
--   </ul>
createDataSourceFromRDS :: Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS

-- | <i>See:</i> <a>createDataSourceFromRDS</a> smart constructor.
data CreateDataSourceFromRDS

-- | A user-supplied name or description of the <tt>DataSource</tt> .
cdsfrdsDataSourceName :: Lens' CreateDataSourceFromRDS (Maybe Text)

-- | The compute statistics for a <tt>DataSource</tt> . The statistics are
--   generated from the observation data referenced by a
--   <tt>DataSource</tt> . Amazon ML uses the statistics internally during
--   <tt>MLModel</tt> training. This parameter must be set to <tt>true</tt>
--   if the DataSourceneeds to be used for <tt>MLModel</tt> training.
cdsfrdsComputeStatistics :: Lens' CreateDataSourceFromRDS (Maybe Bool)

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
--   Typically, an Amazon Resource Number (ARN) becomes the ID for a
--   <tt>DataSource</tt> .
cdsfrdsDataSourceId :: Lens' CreateDataSourceFromRDS Text

-- | The data specification of an Amazon RDS <tt>DataSource</tt> : *
--   DatabaseInformation - * <tt>DatabaseName</tt> - The name of the Amazon
--   RDS database. * <tt>InstanceIdentifier </tt> - A unique identifier for
--   the Amazon RDS database instance. * DatabaseCredentials - AWS Identity
--   and Access Management (IAM) credentials that are used to connect to
--   the Amazon RDS database. * ResourceRole - A role
--   (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry
--   out the copy task from Amazon RDS to Amazon Simple Storage Service
--   (Amazon S3). For more information, see <a>Role templates</a> for data
--   pipelines. * ServiceRole - A role (DataPipelineDefaultRole) assumed by
--   the AWS Data Pipeline service to monitor the progress of the copy task
--   from Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines. * SecurityInfo - The security
--   information to use to access an RDS DB instance. You need to set up
--   appropriate ingress rules for the security entity IDs provided to
--   allow access to the Amazon RDS instance. Specify a [<tt>SubnetId</tt>
--   , <tt>SecurityGroupIds</tt> ] pair for a VPC-based RDS DB instance. *
--   SelectSqlQuery - A query that is used to retrieve the observation data
--   for the <tt>Datasource</tt> . * S3StagingLocation - The Amazon S3
--   location for staging Amazon RDS data. The data retrieved from Amazon
--   RDS using <tt>SelectSqlQuery</tt> is stored in this location. *
--   DataSchemaUri - The Amazon S3 location of the <tt>DataSchema</tt> . *
--   DataSchema - A JSON string representing the schema. This is not
--   required if <tt>DataSchemaUri</tt> is specified. * DataRearrangement -
--   A JSON string that represents the splitting and rearrangement
--   requirements for the <tt>Datasource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt>
cdsfrdsRDSData :: Lens' CreateDataSourceFromRDS RDSDataSpec

-- | The role that Amazon ML assumes on behalf of the user to create and
--   activate a data pipeline in the user's account and copy data using the
--   <tt>SelectSqlQuery</tt> query from Amazon RDS to Amazon S3.
cdsfrdsRoleARN :: Lens' CreateDataSourceFromRDS Text

-- | Creates a value of <a>CreateDataSourceFromRDSResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrdsrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the datasource. This value should be identical to the value
--   of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>cdsfrdsrsResponseStatus</a> - -- | The response status
--   code.</li>
--   </ul>
createDataSourceFromRDSResponse :: Int -> CreateDataSourceFromRDSResponse

-- | Represents the output of a <tt>CreateDataSourceFromRDS</tt> operation,
--   and is an acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateDataSourceFromRDS</tt> &gt; operation is asynchronous.
--   You can poll for updates by using the <tt>GetBatchPrediction</tt>
--   operation and checking the <tt>Status</tt> parameter. You can inspect
--   the <tt>Message</tt> when <tt>Status</tt> shows up as <tt>FAILED</tt>
--   . You can also check the progress of the copy operation by going to
--   the <tt>DataPipeline</tt> console and looking up the pipeline using
--   the <tt>pipelineId </tt> from the describe call.
--   
--   <i>See:</i> <a>createDataSourceFromRDSResponse</a> smart constructor.
data CreateDataSourceFromRDSResponse

-- | A user-supplied ID that uniquely identifies the datasource. This value
--   should be identical to the value of the <tt>DataSourceID</tt> in the
--   request.
cdsfrdsrsDataSourceId :: Lens' CreateDataSourceFromRDSResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cdsfrdsrsResponseStatus :: Lens' CreateDataSourceFromRDSResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS


-- | Generates predictions for a group of observations. The observations to
--   process exist in one or more data files referenced by a
--   <tt>DataSource</tt> . This operation creates a new
--   <tt>BatchPrediction</tt> , and uses an <tt>MLModel</tt> and the data
--   files referenced by the <tt>DataSource</tt> as information sources.
--   
--   <tt>CreateBatchPrediction</tt> is an asynchronous operation. In
--   response to <tt>CreateBatchPrediction</tt> , Amazon Machine Learning
--   (Amazon ML) immediately returns and sets the <tt>BatchPrediction</tt>
--   status to <tt>PENDING</tt> . After the <tt>BatchPrediction</tt>
--   completes, Amazon ML sets the status to <tt>COMPLETED</tt> .
--   
--   You can poll for status updates by using the
--   <tt>GetBatchPrediction</tt> operation and checking the <tt>Status</tt>
--   parameter of the result. After the <tt>COMPLETED</tt> status appears,
--   the results are available in the location specified by the
--   <tt>OutputUri</tt> parameter.
module Network.AWS.MachineLearning.CreateBatchPrediction

-- | Creates a value of <a>CreateBatchPrediction</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cbpBatchPredictionName</a> - A user-supplied name or
--   description of the <tt>BatchPrediction</tt> .
--   <tt>BatchPredictionName</tt> can only use the UTF-8 character
--   set.</li>
--   <li><a>cbpBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> .</li>
--   <li><a>cbpMLModelId</a> - The ID of the <tt>MLModel</tt> that will
--   generate predictions for the group of observations.</li>
--   <li><a>cbpBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that points to the group of observations to
--   predict.</li>
--   <li><a>cbpOutputURI</a> - The location of an Amazon Simple Storage
--   Service (Amazon S3) bucket or directory to store the batch prediction
--   results. The following substrings are not allowed in the <tt>s3
--   key</tt> portion of the <tt>outputURI</tt> field: <tt>:</tt>,
--   <tt>//</tt>, <tt>/./</tt>, <tt>/../</tt>. Amazon ML needs permissions
--   to store and retrieve the logs on your behalf. For information about
--   how to set permissions, see the <a>Amazon Machine Learning Developer
--   Guide</a> .</li>
--   </ul>
createBatchPrediction :: Text -> Text -> Text -> Text -> CreateBatchPrediction

-- | <i>See:</i> <a>createBatchPrediction</a> smart constructor.
data CreateBatchPrediction

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
--   <tt>BatchPredictionName</tt> can only use the UTF-8 character set.
cbpBatchPredictionName :: Lens' CreateBatchPrediction (Maybe Text)

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> .
cbpBatchPredictionId :: Lens' CreateBatchPrediction Text

-- | The ID of the <tt>MLModel</tt> that will generate predictions for the
--   group of observations.
cbpMLModelId :: Lens' CreateBatchPrediction Text

-- | The ID of the <tt>DataSource</tt> that points to the group of
--   observations to predict.
cbpBatchPredictionDataSourceId :: Lens' CreateBatchPrediction Text

-- | The location of an Amazon Simple Storage Service (Amazon S3) bucket or
--   directory to store the batch prediction results. The following
--   substrings are not allowed in the <tt>s3 key</tt> portion of the
--   <tt>outputURI</tt> field: <tt>:</tt>, <tt>//</tt>, <tt>/./</tt>,
--   <tt>/../</tt>. Amazon ML needs permissions to store and retrieve the
--   logs on your behalf. For information about how to set permissions, see
--   the <a>Amazon Machine Learning Developer Guide</a> .
cbpOutputURI :: Lens' CreateBatchPrediction Text

-- | Creates a value of <a>CreateBatchPredictionResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cbprsBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> . This value is identical to
--   the value of the <tt>BatchPredictionId</tt> in the request.</li>
--   <li><a>cbprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createBatchPredictionResponse :: Int -> CreateBatchPredictionResponse

-- | Represents the output of a <tt>CreateBatchPrediction</tt> operation,
--   and is an acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateBatchPrediction</tt> operation is asynchronous. You can
--   poll for status updates by using the <tt>&gt;GetBatchPrediction</tt>
--   operation and checking the <tt>Status</tt> parameter of the result.
--   
--   <i>See:</i> <a>createBatchPredictionResponse</a> smart constructor.
data CreateBatchPredictionResponse

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> . This value is identical to the value of the
--   <tt>BatchPredictionId</tt> in the request.
cbprsBatchPredictionId :: Lens' CreateBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cbprsResponseStatus :: Lens' CreateBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction


-- | Adds one or more tags to an object, up to a limit of 10. Each tag
--   consists of a key and an optional value. If you add a tag using a key
--   that is already associated with the ML object, <tt>AddTags</tt>
--   updates the tag's value.
module Network.AWS.MachineLearning.AddTags

-- | Creates a value of <a>AddTags</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>atTags</a> - The key-value pairs to use to create tags. If you
--   specify a key without specifying a value, Amazon ML creates a tag with
--   the specified key and a value of null.</li>
--   <li><a>atResourceId</a> - The ID of the ML object to tag. For example,
--   <tt>exampleModelId</tt> .</li>
--   <li><a>atResourceType</a> - The type of the ML object to tag.</li>
--   </ul>
addTags :: Text -> TaggableResourceType -> AddTags

-- | <i>See:</i> <a>addTags</a> smart constructor.
data AddTags

-- | The key-value pairs to use to create tags. If you specify a key
--   without specifying a value, Amazon ML creates a tag with the specified
--   key and a value of null.
atTags :: Lens' AddTags [Tag]

-- | The ID of the ML object to tag. For example, <tt>exampleModelId</tt> .
atResourceId :: Lens' AddTags Text

-- | The type of the ML object to tag.
atResourceType :: Lens' AddTags TaggableResourceType

-- | Creates a value of <a>AddTagsResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>atrsResourceId</a> - The ID of the ML object that was
--   tagged.</li>
--   <li><a>atrsResourceType</a> - The type of the ML object that was
--   tagged.</li>
--   <li><a>atrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
addTagsResponse :: Int -> AddTagsResponse

-- | Amazon ML returns the following elements.
--   
--   <i>See:</i> <a>addTagsResponse</a> smart constructor.
data AddTagsResponse

-- | The ID of the ML object that was tagged.
atrsResourceId :: Lens' AddTagsResponse (Maybe Text)

-- | The type of the ML object that was tagged.
atrsResourceType :: Lens' AddTagsResponse (Maybe TaggableResourceType)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
atrsResponseStatus :: Lens' AddTagsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance Data.Data.Data Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.AddTags.AddTags
instance Data.Data.Data Network.AWS.MachineLearning.AddTags.AddTags
instance GHC.Show.Show Network.AWS.MachineLearning.AddTags.AddTags
instance GHC.Read.Read Network.AWS.MachineLearning.AddTags.AddTags
instance GHC.Classes.Eq Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.AddTags.AddTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.AddTags.AddTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.AddTags.AddTags
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.AddTags.AddTags


-- | Updates the <tt>BatchPredictionName</tt> of a <tt>BatchPrediction</tt>
--   .
--   
--   You can use the <tt>GetBatchPrediction</tt> operation to view the
--   contents of the updated data element.
module Network.AWS.MachineLearning.UpdateBatchPrediction

-- | Creates a value of <a>UpdateBatchPrediction</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ubpBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> during creation.</li>
--   <li><a>ubpBatchPredictionName</a> - A new user-supplied name or
--   description of the <tt>BatchPrediction</tt> .</li>
--   </ul>
updateBatchPrediction :: Text -> Text -> UpdateBatchPrediction

-- | <i>See:</i> <a>updateBatchPrediction</a> smart constructor.
data UpdateBatchPrediction

-- | The ID assigned to the <tt>BatchPrediction</tt> during creation.
ubpBatchPredictionId :: Lens' UpdateBatchPrediction Text

-- | A new user-supplied name or description of the
--   <tt>BatchPrediction</tt> .
ubpBatchPredictionName :: Lens' UpdateBatchPrediction Text

-- | Creates a value of <a>UpdateBatchPredictionResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ubprsBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> during creation. This value should be
--   identical to the value of the <tt>BatchPredictionId</tt> in the
--   request.</li>
--   <li><a>ubprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateBatchPredictionResponse :: Int -> UpdateBatchPredictionResponse

-- | Represents the output of an <tt>UpdateBatchPrediction</tt> operation.
--   
--   You can see the updated content by using the
--   <tt>GetBatchPrediction</tt> operation.
--   
--   <i>See:</i> <a>updateBatchPredictionResponse</a> smart constructor.
data UpdateBatchPredictionResponse

-- | The ID assigned to the <tt>BatchPrediction</tt> during creation. This
--   value should be identical to the value of the
--   <tt>BatchPredictionId</tt> in the request.
ubprsBatchPredictionId :: Lens' UpdateBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
ubprsResponseStatus :: Lens' UpdateBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction


-- | Updates the <tt>DataSourceName</tt> of a <tt>DataSource</tt> .
--   
--   You can use the <tt>GetDataSource</tt> operation to view the contents
--   of the updated data element.
module Network.AWS.MachineLearning.UpdateDataSource

-- | Creates a value of <a>UpdateDataSource</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>udsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> during creation.</li>
--   <li><a>udsDataSourceName</a> - A new user-supplied name or description
--   of the <tt>DataSource</tt> that will replace the current
--   description.</li>
--   </ul>
updateDataSource :: Text -> Text -> UpdateDataSource

-- | <i>See:</i> <a>updateDataSource</a> smart constructor.
data UpdateDataSource

-- | The ID assigned to the <tt>DataSource</tt> during creation.
udsDataSourceId :: Lens' UpdateDataSource Text

-- | A new user-supplied name or description of the <tt>DataSource</tt>
--   that will replace the current description.
udsDataSourceName :: Lens' UpdateDataSource Text

-- | Creates a value of <a>UpdateDataSourceResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>udsrsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> during creation. This value should be identical to
--   the value of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>udsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateDataSourceResponse :: Int -> UpdateDataSourceResponse

-- | Represents the output of an <tt>UpdateDataSource</tt> operation.
--   
--   You can see the updated content by using the
--   <tt>GetBatchPrediction</tt> operation.
--   
--   <i>See:</i> <a>updateDataSourceResponse</a> smart constructor.
data UpdateDataSourceResponse

-- | The ID assigned to the <tt>DataSource</tt> during creation. This value
--   should be identical to the value of the <tt>DataSourceID</tt> in the
--   request.
udsrsDataSourceId :: Lens' UpdateDataSourceResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
udsrsResponseStatus :: Lens' UpdateDataSourceResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Data.Data.Data Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource


-- | Updates the <tt>EvaluationName</tt> of an <tt>Evaluation</tt> .
--   
--   You can use the <tt>GetEvaluation</tt> operation to view the contents
--   of the updated data element.
module Network.AWS.MachineLearning.UpdateEvaluation

-- | Creates a value of <a>UpdateEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ueEvaluationId</a> - The ID assigned to the <tt>Evaluation</tt>
--   during creation.</li>
--   <li><a>ueEvaluationName</a> - A new user-supplied name or description
--   of the <tt>Evaluation</tt> that will replace the current content.</li>
--   </ul>
updateEvaluation :: Text -> Text -> UpdateEvaluation

-- | <i>See:</i> <a>updateEvaluation</a> smart constructor.
data UpdateEvaluation

-- | The ID assigned to the <tt>Evaluation</tt> during creation.
ueEvaluationId :: Lens' UpdateEvaluation Text

-- | A new user-supplied name or description of the <tt>Evaluation</tt>
--   that will replace the current content.
ueEvaluationName :: Lens' UpdateEvaluation Text

-- | Creates a value of <a>UpdateEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>uersEvaluationId</a> - The ID assigned to the
--   <tt>Evaluation</tt> during creation. This value should be identical to
--   the value of the <tt>Evaluation</tt> in the request.</li>
--   <li><a>uersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateEvaluationResponse :: Int -> UpdateEvaluationResponse

-- | Represents the output of an <tt>UpdateEvaluation</tt> operation.
--   
--   You can see the updated content by using the <tt>GetEvaluation</tt>
--   operation.
--   
--   <i>See:</i> <a>updateEvaluationResponse</a> smart constructor.
data UpdateEvaluationResponse

-- | The ID assigned to the <tt>Evaluation</tt> during creation. This value
--   should be identical to the value of the <tt>Evaluation</tt> in the
--   request.
uersEvaluationId :: Lens' UpdateEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
uersResponseStatus :: Lens' UpdateEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation


-- | Updates the <tt>MLModelName</tt> and the <tt>ScoreThreshold</tt> of an
--   <tt>MLModel</tt> .
--   
--   You can use the <tt>GetMLModel</tt> operation to view the contents of
--   the updated data element.
module Network.AWS.MachineLearning.UpdateMLModel

-- | Creates a value of <a>UpdateMLModel</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>umlmMLModelName</a> - A user-supplied name or description of
--   the <tt>MLModel</tt> .</li>
--   <li><a>umlmScoreThreshold</a> - The <tt>ScoreThreshold</tt> used in
--   binary classification <tt>MLModel</tt> that marks the boundary between
--   a positive prediction and a negative prediction. Output values greater
--   than or equal to the <tt>ScoreThreshold</tt> receive a positive result
--   from the <tt>MLModel</tt> , such as <tt>true</tt> . Output values less
--   than the <tt>ScoreThreshold</tt> receive a negative response from the
--   <tt>MLModel</tt> , such as <tt>false</tt> .</li>
--   <li><a>umlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation.</li>
--   </ul>
updateMLModel :: Text -> UpdateMLModel

-- | <i>See:</i> <a>updateMLModel</a> smart constructor.
data UpdateMLModel

-- | A user-supplied name or description of the <tt>MLModel</tt> .
umlmMLModelName :: Lens' UpdateMLModel (Maybe Text)

-- | The <tt>ScoreThreshold</tt> used in binary classification
--   <tt>MLModel</tt> that marks the boundary between a positive prediction
--   and a negative prediction. Output values greater than or equal to the
--   <tt>ScoreThreshold</tt> receive a positive result from the
--   <tt>MLModel</tt> , such as <tt>true</tt> . Output values less than the
--   <tt>ScoreThreshold</tt> receive a negative response from the
--   <tt>MLModel</tt> , such as <tt>false</tt> .
umlmScoreThreshold :: Lens' UpdateMLModel (Maybe Double)

-- | The ID assigned to the <tt>MLModel</tt> during creation.
umlmMLModelId :: Lens' UpdateMLModel Text

-- | Creates a value of <a>UpdateMLModelResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>umlmrsMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation. This value should be identical to the value of the
--   <tt>MLModelID</tt> in the request.</li>
--   <li><a>umlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateMLModelResponse :: Int -> UpdateMLModelResponse

-- | Represents the output of an <tt>UpdateMLModel</tt> operation.
--   
--   You can see the updated content by using the <tt>GetMLModel</tt>
--   operation.
--   
--   <i>See:</i> <a>updateMLModelResponse</a> smart constructor.
data UpdateMLModelResponse

-- | The ID assigned to the <tt>MLModel</tt> during creation. This value
--   should be identical to the value of the <tt>MLModelID</tt> in the
--   request.
umlmrsMLModelId :: Lens' UpdateMLModelResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
umlmrsResponseStatus :: Lens' UpdateMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Data.Data.Data Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel


module Network.AWS.MachineLearning.Waiters

-- | Polls <a>DescribeMLModels</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
mLModelAvailable :: Wait DescribeMLModels

-- | Polls <a>DescribeBatchPredictions</a> every 30 seconds until a
--   successful state is reached. An error is returned after 60 failed
--   checks.
batchPredictionAvailable :: Wait DescribeBatchPredictions

-- | Polls <a>DescribeDataSources</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
dataSourceAvailable :: Wait DescribeDataSources

-- | Polls <a>DescribeEvaluations</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
evaluationAvailable :: Wait DescribeEvaluations


-- | Definition of the public APIs exposed by Amazon Machine Learning
module Network.AWS.MachineLearning

-- | API version <tt>2014-12-12</tt> of the Amazon Machine Learning SDK
--   configuration.
machineLearning :: Service

-- | Prism for InvalidTagException' errors.
_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the server occurred when trying to process a request.
_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the client occurred. Typically, the cause is an invalid
--   input value.
_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A second request to use or change an object was not allowed. This can
--   result from retrying a request using a parameter that was not present
--   in the original request.
_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError

-- | Prism for TagLimitExceededException' errors.
_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The exception is thrown when a predict request is made to an unmounted
--   <tt>MLModel</tt> .
_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A specified resource cannot be located.
_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The subscriber exceeded the maximum number of operations. This
--   exception can occur when listing objects such as <tt>DataSource</tt> .
_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | Polls <a>DescribeMLModels</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
mLModelAvailable :: Wait DescribeMLModels

-- | Polls <a>DescribeBatchPredictions</a> every 30 seconds until a
--   successful state is reached. An error is returned after 60 failed
--   checks.
batchPredictionAvailable :: Wait DescribeBatchPredictions

-- | Polls <a>DescribeDataSources</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
dataSourceAvailable :: Wait DescribeDataSources

-- | Polls <a>DescribeEvaluations</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
evaluationAvailable :: Wait DescribeEvaluations

-- | The function used to train an <tt>MLModel</tt> . Training choices
--   supported by Amazon ML include the following:
--   
--   <ul>
--   <li><tt>SGD</tt> - Stochastic Gradient Descent. *
--   <tt>RandomForest</tt> - Random forest of decision trees.</li>
--   </ul>
data Algorithm
SGD :: Algorithm

-- | A list of the variables to use in searching or filtering
--   <tt>BatchPrediction</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>BatchPrediction</tt> creation date. * <tt>Status</tt> - Sets the
--   search criteria to <tt>BatchPrediction</tt> status. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>BatchPrediction</tt>
--   ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage Service
--   (Amazon S3) bucket or directory.</li>
--   </ul>
data BatchPredictionFilterVariable
BatchCreatedAt :: BatchPredictionFilterVariable
BatchDataSourceId :: BatchPredictionFilterVariable
BatchDataURI :: BatchPredictionFilterVariable
BatchIAMUser :: BatchPredictionFilterVariable
BatchLastUpdatedAt :: BatchPredictionFilterVariable
BatchMLModelId :: BatchPredictionFilterVariable
BatchName :: BatchPredictionFilterVariable
BatchStatus :: BatchPredictionFilterVariable

-- | A list of the variables to use in searching or filtering
--   <tt>DataSource</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>DataSource</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>DataSource</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>DataSource</tt> ____
--   <tt>Name</tt> . * <tt>DataUri</tt> - Sets the search criteria to the
--   URI of data files used to create the <tt>DataSource</tt> . The URI can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory. * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>DataSource</tt> creation.</li>
--   </ul>
data DataSourceFilterVariable
DataCreatedAt :: DataSourceFilterVariable
DataDATALOCATIONS3 :: DataSourceFilterVariable
DataIAMUser :: DataSourceFilterVariable
DataLastUpdatedAt :: DataSourceFilterVariable
DataName :: DataSourceFilterVariable
DataStatus :: DataSourceFilterVariable

-- | Contains the key values of <tt>DetailsMap</tt> :
--   <tt>PredictiveModelType</tt> - Indicates the type of the
--   <tt>MLModel</tt> . <tt>Algorithm</tt> - Indicates the algorithm that
--   was used for the <tt>MLModel</tt> .
data DetailsAttributes
Algorithm :: DetailsAttributes
PredictiveModelType :: DetailsAttributes

-- | Object status with the following possible values:
--   
--   <ul>
--   <li><tt>PENDING</tt> * <tt>INPROGRESS</tt> * <tt>FAILED</tt> *
--   <tt>COMPLETED</tt> * <tt>DELETED</tt></li>
--   </ul>
data EntityStatus
ESCompleted :: EntityStatus
ESDeleted :: EntityStatus
ESFailed :: EntityStatus
ESInprogress :: EntityStatus
ESPending :: EntityStatus

-- | A list of the variables to use in searching or filtering
--   <tt>Evaluation</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>Evaluation</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>Evaluation</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>Evaluation</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked an evaluation. * <tt>MLModelId</tt> - Sets
--   the search criteria to the <tt>Predictor</tt> that was evaluated. *
--   <tt>DataSourceId</tt> - Sets the search criteria to the
--   <tt>DataSource</tt> used in evaluation. * <tt>DataUri</tt> - Sets the
--   search criteria to the data file(s) used in evaluation. The URL can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory.</li>
--   </ul>
data EvaluationFilterVariable
EvalCreatedAt :: EvaluationFilterVariable
EvalDataSourceId :: EvaluationFilterVariable
EvalDataURI :: EvaluationFilterVariable
EvalIAMUser :: EvaluationFilterVariable
EvalLastUpdatedAt :: EvaluationFilterVariable
EvalMLModelId :: EvaluationFilterVariable
EvalName :: EvaluationFilterVariable
EvalStatus :: EvaluationFilterVariable
data MLModelFilterVariable
MLMFVAlgorithm :: MLModelFilterVariable
MLMFVCreatedAt :: MLModelFilterVariable
MLMFVIAMUser :: MLModelFilterVariable
MLMFVLastUpdatedAt :: MLModelFilterVariable
MLMFVMLModelType :: MLModelFilterVariable
MLMFVName :: MLModelFilterVariable
MLMFVRealtimeEndpointStatus :: MLModelFilterVariable
MLMFVStatus :: MLModelFilterVariable
MLMFVTrainingDataSourceId :: MLModelFilterVariable
MLMFVTrainingDataURI :: MLModelFilterVariable
data MLModelType
Binary :: MLModelType
Multiclass :: MLModelType
Regression :: MLModelType
data RealtimeEndpointStatus
Failed :: RealtimeEndpointStatus
None :: RealtimeEndpointStatus
Ready :: RealtimeEndpointStatus
Updating :: RealtimeEndpointStatus

-- | The sort order specified in a listing condition. Possible values
--   include the following:
--   
--   <ul>
--   <li><tt>asc</tt> - Present the information in ascending order (from
--   A-Z). * <tt>dsc</tt> - Present the information in descending order
--   (from Z-A).</li>
--   </ul>
data SortOrder
Asc :: SortOrder
Dsc :: SortOrder
data TaggableResourceType
BatchPrediction :: TaggableResourceType
DataSource :: TaggableResourceType
Evaluation :: TaggableResourceType
MLModel :: TaggableResourceType

-- | Represents the output of a <tt>GetBatchPrediction</tt> operation.
--   
--   The content consists of the detailed metadata, the status, and the
--   data file information of a <tt>Batch Prediction</tt> .
--   
--   <i>See:</i> <a>batchPrediction</a> smart constructor.
data BatchPrediction

-- | Creates a value of <a>BatchPrediction</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>bpStatus</a> - The status of the <tt>BatchPrediction</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to generate
--   predictions for a batch of observations. * <tt>INPROGRESS</tt> - The
--   process is underway. * <tt>FAILED</tt> - The request to perform a
--   batch prediction did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The batch prediction process completed
--   successfully. * <tt>DELETED</tt> - The <tt>BatchPrediction</tt> is
--   marked as deleted. It is not usable.</li>
--   <li><a>bpLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>bpCreatedAt</a> - The time that the <tt>BatchPrediction</tt>
--   was created. The time is expressed in epoch time.</li>
--   <li><a>bpComputeTime</a> - Undocumented member.</li>
--   <li><a>bpInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>bpMLModelId</a> - The ID of the <tt>MLModel</tt> that generated
--   predictions for the <tt>BatchPrediction</tt> request.</li>
--   <li><a>bpBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that points to the group of observations to
--   predict.</li>
--   <li><a>bpTotalRecordCount</a> - Undocumented member.</li>
--   <li><a>bpStartedAt</a> - Undocumented member.</li>
--   <li><a>bpBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> at creation. This value should be identical
--   to the value of the <tt>BatchPredictionID</tt> in the request.</li>
--   <li><a>bpFinishedAt</a> - Undocumented member.</li>
--   <li><a>bpInvalidRecordCount</a> - Undocumented member.</li>
--   <li><a>bpCreatedByIAMUser</a> - The AWS user account that invoked the
--   <tt>BatchPrediction</tt> . The account type can be either an AWS root
--   account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>bpName</a> - A user-supplied name or description of the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>bpMessage</a> - A description of the most recent details about
--   processing the batch prediction request.</li>
--   <li><a>bpOutputURI</a> - The location of an Amazon S3 bucket or
--   directory to receive the operation results. The following substrings
--   are not allowed in the <tt>s3 key</tt> portion of the
--   <tt>outputURI</tt> field: <tt>:</tt>, <tt>//</tt>, <tt>/./</tt>,
--   <tt>/../</tt>.</li>
--   </ul>
batchPrediction :: BatchPrediction

-- | The status of the <tt>BatchPrediction</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to generate predictions for a batch of
--   observations. * <tt>INPROGRESS</tt> - The process is underway. *
--   <tt>FAILED</tt> - The request to perform a batch prediction did not
--   run to completion. It is not usable. * <tt>COMPLETED</tt> - The batch
--   prediction process completed successfully. * <tt>DELETED</tt> - The
--   <tt>BatchPrediction</tt> is marked as deleted. It is not usable.
bpStatus :: Lens' BatchPrediction (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The time that the <tt>BatchPrediction</tt> was created. The time is
--   expressed in epoch time.
bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpComputeTime :: Lens' BatchPrediction (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that generated predictions for the
--   <tt>BatchPrediction</tt> request.
bpMLModelId :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that points to the group of
--   observations to predict.
bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | Undocumented member.
bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The ID assigned to the <tt>BatchPrediction</tt> at creation. This
--   value should be identical to the value of the
--   <tt>BatchPredictionID</tt> in the request.
bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | The AWS user account that invoked the <tt>BatchPrediction</tt> . The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text)

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
bpName :: Lens' BatchPrediction (Maybe Text)

-- | A description of the most recent details about processing the batch
--   prediction request.
bpMessage :: Lens' BatchPrediction (Maybe Text)

-- | The location of an Amazon S3 bucket or directory to receive the
--   operation results. The following substrings are not allowed in the
--   <tt>s3 key</tt> portion of the <tt>outputURI</tt> field: <tt>:</tt>,
--   <tt>//</tt>, <tt>/./</tt>, <tt>/../</tt>.
bpOutputURI :: Lens' BatchPrediction (Maybe Text)

-- | Represents the output of the <tt>GetDataSource</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>dataSource</a> smart constructor.
data DataSource

-- | Creates a value of <a>DataSource</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dsStatus</a> - The current status of the <tt>DataSource</tt> .
--   This element can have one of the following values: * PENDING - Amazon
--   Machine Learning (Amazon ML) submitted a request to create a
--   <tt>DataSource</tt> . * INPROGRESS - The creation process is underway.
--   * FAILED - The request to create a <tt>DataSource</tt> did not run to
--   completion. It is not usable. * COMPLETED - The creation process
--   completed successfully. * DELETED - The <tt>DataSource</tt> is marked
--   as deleted. It is not usable.</li>
--   <li><a>dsNumberOfFiles</a> - The number of data files referenced by
--   the <tt>DataSource</tt> .</li>
--   <li><a>dsLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>dsCreatedAt</a> - The time that the <tt>DataSource</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>dsComputeTime</a> - Undocumented member.</li>
--   <li><a>dsDataSourceId</a> - The ID that is assigned to the
--   <tt>DataSource</tt> during creation.</li>
--   <li><a>dsRDSMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataSizeInBytes</a> - The total number of observations
--   contained in the data files that the <tt>DataSource</tt>
--   references.</li>
--   <li><a>dsStartedAt</a> - Undocumented member.</li>
--   <li><a>dsFinishedAt</a> - Undocumented member.</li>
--   <li><a>dsCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>DataSource</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>dsName</a> - A user-supplied name or description of the
--   <tt>DataSource</tt> .</li>
--   <li><a>dsDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Service (Amazon S3) that is used by a
--   <tt>DataSource</tt> .</li>
--   <li><a>dsComputeStatistics</a> - The parameter is <tt>true</tt> if
--   statistics need to be generated from the observation data.</li>
--   <li><a>dsMessage</a> - A description of the most recent details about
--   creating the <tt>DataSource</tt> .</li>
--   <li><a>dsRedshiftMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement requirement used when this
--   <tt>DataSource</tt> was created.</li>
--   <li><a>dsRoleARN</a> - Undocumented member.</li>
--   </ul>
dataSource :: DataSource

-- | The current status of the <tt>DataSource</tt> . This element can have
--   one of the following values: * PENDING - Amazon Machine Learning
--   (Amazon ML) submitted a request to create a <tt>DataSource</tt> . *
--   INPROGRESS - The creation process is underway. * FAILED - The request
--   to create a <tt>DataSource</tt> did not run to completion. It is not
--   usable. * COMPLETED - The creation process completed successfully. *
--   DELETED - The <tt>DataSource</tt> is marked as deleted. It is not
--   usable.
dsStatus :: Lens' DataSource (Maybe EntityStatus)

-- | The number of data files referenced by the <tt>DataSource</tt> .
dsNumberOfFiles :: Lens' DataSource (Maybe Integer)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime)

-- | The time that the <tt>DataSource</tt> was created. The time is
--   expressed in epoch time.
dsCreatedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsComputeTime :: Lens' DataSource (Maybe Integer)

-- | The ID that is assigned to the <tt>DataSource</tt> during creation.
dsDataSourceId :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata)

-- | The total number of observations contained in the data files that the
--   <tt>DataSource</tt> references.
dsDataSizeInBytes :: Lens' DataSource (Maybe Integer)

-- | Undocumented member.
dsStartedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsFinishedAt :: Lens' DataSource (Maybe UTCTime)

-- | The AWS user account from which the <tt>DataSource</tt> was created.
--   The account type can be either an AWS root account or an AWS Identity
--   and Access Management (IAM) user account.
dsCreatedByIAMUser :: Lens' DataSource (Maybe Text)

-- | A user-supplied name or description of the <tt>DataSource</tt> .
dsName :: Lens' DataSource (Maybe Text)

-- | The location and name of the data in Amazon Simple Storage Service
--   (Amazon S3) that is used by a <tt>DataSource</tt> .
dsDataLocationS3 :: Lens' DataSource (Maybe Text)

-- | The parameter is <tt>true</tt> if statistics need to be generated from
--   the observation data.
dsComputeStatistics :: Lens' DataSource (Maybe Bool)

-- | A description of the most recent details about creating the
--   <tt>DataSource</tt> .
dsMessage :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)

-- | A JSON string that represents the splitting and rearrangement
--   requirement used when this <tt>DataSource</tt> was created.
dsDataRearrangement :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRoleARN :: Lens' DataSource (Maybe Text)

-- | Represents the output of <tt>GetEvaluation</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>evaluation</a> smart constructor.
data Evaluation

-- | Creates a value of <a>Evaluation</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>eStatus</a> - The status of the evaluation. This element can
--   have one of the following values: * <tt>PENDING</tt> - Amazon Machine
--   Learning (Amazon ML) submitted a request to evaluate an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The evaluation is underway.
--   * <tt>FAILED</tt> - The request to evaluate an <tt>MLModel</tt> did
--   not run to completion. It is not usable. * <tt>COMPLETED</tt> - The
--   evaluation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>Evaluation</tt> is marked as deleted. It is not usable.</li>
--   <li><a>ePerformanceMetrics</a> - Measurements of how well the
--   <tt>MLModel</tt> performed, using observations referenced by the
--   <tt>DataSource</tt> . One of the following metrics is returned, based
--   on the type of the <tt>MLModel</tt> : * BinaryAUC: A binary
--   <tt>MLModel</tt> uses the Area Under the Curve (AUC) technique to
--   measure performance. * RegressionRMSE: A regression <tt>MLModel</tt>
--   uses the Root Mean Square Error (RMSE) technique to measure
--   performance. RMSE measures the difference between predicted and actual
--   values for a single variable. * MulticlassAvgFScore: A multiclass
--   <tt>MLModel</tt> uses the F1 score technique to measure performance.
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .</li>
--   <li><a>eLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>Evaluation</tt> . The time is expressed in epoch time.</li>
--   <li><a>eCreatedAt</a> - The time that the <tt>Evaluation</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>eComputeTime</a> - Undocumented member.</li>
--   <li><a>eInputDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Server (Amazon S3) that is used in the
--   evaluation.</li>
--   <li><a>eMLModelId</a> - The ID of the <tt>MLModel</tt> that is the
--   focus of the evaluation.</li>
--   <li><a>eStartedAt</a> - Undocumented member.</li>
--   <li><a>eFinishedAt</a> - Undocumented member.</li>
--   <li><a>eCreatedByIAMUser</a> - The AWS user account that invoked the
--   evaluation. The account type can be either an AWS root account or an
--   AWS Identity and Access Management (IAM) user account.</li>
--   <li><a>eName</a> - A user-supplied name or description of the
--   <tt>Evaluation</tt> .</li>
--   <li><a>eEvaluationId</a> - The ID that is assigned to the
--   <tt>Evaluation</tt> at creation.</li>
--   <li><a>eMessage</a> - A description of the most recent details about
--   evaluating the <tt>MLModel</tt> .</li>
--   <li><a>eEvaluationDataSourceId</a> - The ID of the <tt>DataSource</tt>
--   that is used to evaluate the <tt>MLModel</tt> .</li>
--   </ul>
evaluation :: Evaluation

-- | The status of the evaluation. This element can have one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Learning (Amazon
--   ML) submitted a request to evaluate an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The evaluation is underway. * <tt>FAILED</tt> -
--   The request to evaluate an <tt>MLModel</tt> did not run to completion.
--   It is not usable. * <tt>COMPLETED</tt> - The evaluation process
--   completed successfully. * <tt>DELETED</tt> - The <tt>Evaluation</tt>
--   is marked as deleted. It is not usable.
eStatus :: Lens' Evaluation (Maybe EntityStatus)

-- | Measurements of how well the <tt>MLModel</tt> performed, using
--   observations referenced by the <tt>DataSource</tt> . One of the
--   following metrics is returned, based on the type of the
--   <tt>MLModel</tt> : * BinaryAUC: A binary <tt>MLModel</tt> uses the
--   Area Under the Curve (AUC) technique to measure performance. *
--   RegressionRMSE: A regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable. * MulticlassAvgFScore: A multiclass <tt>MLModel</tt> uses
--   the F1 score technique to measure performance. For more information
--   about performance metrics, please see the <a>Amazon Machine Learning
--   Developer Guide</a> .
ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)

-- | The time of the most recent edit to the <tt>Evaluation</tt> . The time
--   is expressed in epoch time.
eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The time that the <tt>Evaluation</tt> was created. The time is
--   expressed in epoch time.
eCreatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eComputeTime :: Lens' Evaluation (Maybe Integer)

-- | The location and name of the data in Amazon Simple Storage Server
--   (Amazon S3) that is used in the evaluation.
eInputDataLocationS3 :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that is the focus of the evaluation.
eMLModelId :: Lens' Evaluation (Maybe Text)

-- | Undocumented member.
eStartedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eFinishedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The AWS user account that invoked the evaluation. The account type can
--   be either an AWS root account or an AWS Identity and Access Management
--   (IAM) user account.
eCreatedByIAMUser :: Lens' Evaluation (Maybe Text)

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
eName :: Lens' Evaluation (Maybe Text)

-- | The ID that is assigned to the <tt>Evaluation</tt> at creation.
eEvaluationId :: Lens' Evaluation (Maybe Text)

-- | A description of the most recent details about evaluating the
--   <tt>MLModel</tt> .
eMessage :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that is used to evaluate the
--   <tt>MLModel</tt> .
eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text)

-- | Represents the output of a <tt>GetMLModel</tt> operation.
--   
--   The content consists of the detailed metadata and the current status
--   of the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>mLModel</a> smart constructor.
data MLModel

-- | Creates a value of <a>MLModel</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>mlmStatus</a> - The current status of an <tt>MLModel</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to create an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The creation process is
--   underway. * <tt>FAILED</tt> - The request to create an
--   <tt>MLModel</tt> didn't run to completion. The model isn't usable. *
--   <tt>COMPLETED</tt> - The creation process completed successfully. *
--   <tt>DELETED</tt> - The <tt>MLModel</tt> is marked as deleted. It isn't
--   usable.</li>
--   <li><a>mlmLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>MLModel</tt> . The time is expressed in epoch time.</li>
--   <li><a>mlmTrainingParameters</a> - A list of the training parameters
--   in the <tt>MLModel</tt> . The list is implemented as a map of
--   key-value pairs. The following is the current set of training
--   parameters: * <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed
--   size of the model. Depending on the input data, the size of the model
--   might affect its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>mlmScoreThresholdLastUpdatedAt</a> - The time of the most
--   recent edit to the <tt>ScoreThreshold</tt> . The time is expressed in
--   epoch time.</li>
--   <li><a>mlmCreatedAt</a> - The time that the <tt>MLModel</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>mlmComputeTime</a> - Undocumented member.</li>
--   <li><a>mlmInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>mlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt> at
--   creation.</li>
--   <li><a>mlmSizeInBytes</a> - Undocumented member.</li>
--   <li><a>mlmStartedAt</a> - Undocumented member.</li>
--   <li><a>mlmScoreThreshold</a> - Undocumented member.</li>
--   <li><a>mlmFinishedAt</a> - Undocumented member.</li>
--   <li><a>mlmAlgorithm</a> - The algorithm used to train the
--   <tt>MLModel</tt> . The following algorithm is supported: *
--   <tt>SGD</tt> -- Stochastic gradient descent. The goal of <tt>SGD</tt>
--   is to minimize the gradient of the loss function.</li>
--   <li><a>mlmCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>MLModel</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>mlmName</a> - A user-supplied name or description of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmEndpointInfo</a> - The current endpoint of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmTrainingDataSourceId</a> - The ID of the training
--   <tt>DataSource</tt> . The <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .</li>
--   <li><a>mlmMessage</a> - A description of the most recent details about
--   accessing the <tt>MLModel</tt> .</li>
--   <li><a>mlmMLModelType</a> - Identifies the <tt>MLModel</tt> category.
--   The following are the available types: * <tt>REGRESSION</tt> -
--   Produces a numeric result. For example, "What price should a house be
--   listed at?" * <tt>BINARY</tt> - Produces one of two possible results.
--   For example, "Is this a child-friendly web site?". *
--   <tt>MULTICLASS</tt> - Produces one of several possible results. For
--   example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".</li>
--   </ul>
mLModel :: MLModel

-- | The current status of an <tt>MLModel</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to create an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The creation process is underway. *
--   <tt>FAILED</tt> - The request to create an <tt>MLModel</tt> didn't run
--   to completion. The model isn't usable. * <tt>COMPLETED</tt> - The
--   creation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>MLModel</tt> is marked as deleted. It isn't usable.
mlmStatus :: Lens' MLModel (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>MLModel</tt> . The time is
--   expressed in epoch time.
mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
mlmTrainingParameters :: Lens' MLModel (HashMap Text Text)

-- | The time of the most recent edit to the <tt>ScoreThreshold</tt> . The
--   time is expressed in epoch time.
mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | The time that the <tt>MLModel</tt> was created. The time is expressed
--   in epoch time.
mlmCreatedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmComputeTime :: Lens' MLModel (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text)

-- | The ID assigned to the <tt>MLModel</tt> at creation.
mlmMLModelId :: Lens' MLModel (Maybe Text)

-- | Undocumented member.
mlmSizeInBytes :: Lens' MLModel (Maybe Integer)

-- | Undocumented member.
mlmStartedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmScoreThreshold :: Lens' MLModel (Maybe Double)

-- | Undocumented member.
mlmFinishedAt :: Lens' MLModel (Maybe UTCTime)

-- | The algorithm used to train the <tt>MLModel</tt> . The following
--   algorithm is supported: * <tt>SGD</tt> -- Stochastic gradient descent.
--   The goal of <tt>SGD</tt> is to minimize the gradient of the loss
--   function.
mlmAlgorithm :: Lens' MLModel (Maybe Algorithm)

-- | The AWS user account from which the <tt>MLModel</tt> was created. The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
mlmName :: Lens' MLModel (Maybe Text)

-- | The current endpoint of the <tt>MLModel</tt> .
mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)

-- | The ID of the training <tt>DataSource</tt> . The
--   <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .
mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text)

-- | A description of the most recent details about accessing the
--   <tt>MLModel</tt> .
mlmMessage :: Lens' MLModel (Maybe Text)

-- | Identifies the <tt>MLModel</tt> category. The following are the
--   available types: * <tt>REGRESSION</tt> - Produces a numeric result.
--   For example, "What price should a house be listed at?" *
--   <tt>BINARY</tt> - Produces one of two possible results. For example,
--   "Is this a child-friendly web site?". * <tt>MULTICLASS</tt> - Produces
--   one of several possible results. For example, "Is this a HIGH-, LOW-,
--   or MEDIUM-risk trade?".
mlmMLModelType :: Lens' MLModel (Maybe MLModelType)

-- | Measurements of how well the <tt>MLModel</tt> performed on known
--   observations. One of the following metrics is returned, based on the
--   type of the <tt>MLModel</tt> :
--   
--   <ul>
--   <li>BinaryAUC: The binary <tt>MLModel</tt> uses the Area Under the
--   Curve (AUC) technique to measure performance.</li>
--   <li>RegressionRMSE: The regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable.</li>
--   <li>MulticlassAvgFScore: The multiclass <tt>MLModel</tt> uses the F1
--   score technique to measure performance.</li>
--   </ul>
--   
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .
--   
--   <i>See:</i> <a>performanceMetrics</a> smart constructor.
data PerformanceMetrics

-- | Creates a value of <a>PerformanceMetrics</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pmProperties</a> - Undocumented member.</li>
--   </ul>
performanceMetrics :: PerformanceMetrics

-- | Undocumented member.
pmProperties :: Lens' PerformanceMetrics (HashMap Text Text)

-- | The output from a <tt>Predict</tt> operation:
--   
--   <ul>
--   <li><tt>Details</tt> - Contains the following attributes:
--   <tt>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY |
--   MULTICLASS</tt> <tt>DetailsAttributes.ALGORITHM - SGD</tt></li>
--   <li><tt>PredictedLabel</tt> - Present for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> request.</li>
--   <li><tt>PredictedScores</tt> - Contains the raw classification score
--   corresponding to each label.</li>
--   <li><tt>PredictedValue</tt> - Present for a <tt>REGRESSION</tt>
--   <tt>MLModel</tt> request.</li>
--   </ul>
--   
--   <i>See:</i> <a>prediction</a> smart constructor.
data Prediction

-- | Creates a value of <a>Prediction</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pPredictedValue</a> - The prediction value for
--   <tt>REGRESSION</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedLabel</a> - The prediction label for either a
--   <tt>BINARY</tt> or <tt>MULTICLASS</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedScores</a> - Undocumented member.</li>
--   <li><a>pDetails</a> - Undocumented member.</li>
--   </ul>
prediction :: Prediction

-- | The prediction value for <tt>REGRESSION</tt> <tt>MLModel</tt> .
pPredictedValue :: Lens' Prediction (Maybe Double)

-- | The prediction label for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> .
pPredictedLabel :: Lens' Prediction (Maybe Text)

-- | Undocumented member.
pPredictedScores :: Lens' Prediction (HashMap Text Double)

-- | Undocumented member.
pDetails :: Lens' Prediction (HashMap DetailsAttributes Text)

-- | The data specification of an Amazon Relational Database Service
--   (Amazon RDS) <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>rdsDataSpec</a> smart constructor.
data RDSDataSpec

-- | Creates a value of <a>RDSDataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdsDataSchemaURI</a> - The Amazon S3 location of the
--   <tt>DataSchema</tt> .</li>
--   <li><a>rdsdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon RDS <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rdsdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rdsdsDatabaseInformation</a> - Describes the
--   <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of an Amazon RDS
--   database.</li>
--   <li><a>rdsdsSelectSqlQuery</a> - The query that is used to retrieve
--   the observation data for the <tt>DataSource</tt> .</li>
--   <li><a>rdsdsDatabaseCredentials</a> - The AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon RDS
--   database.</li>
--   <li><a>rdsdsS3StagingLocation</a> - The Amazon S3 location for staging
--   Amazon RDS data. The data retrieved from Amazon RDS using
--   <tt>SelectSqlQuery</tt> is stored in this location.</li>
--   <li><a>rdsdsResourceRole</a> - The role
--   (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute
--   Cloud (Amazon EC2) instance to carry out the copy operation from
--   Amazon RDS to an Amazon S3 task. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rdsdsServiceRole</a> - The role (DataPipelineDefaultRole)
--   assumed by AWS Data Pipeline service to monitor the progress of the
--   copy task from Amazon RDS to Amazon S3. For more information, see
--   <a>Role templates</a> for data pipelines.</li>
--   <li><a>rdsdsSubnetId</a> - The subnet ID to be used to access a
--   VPC-based RDS DB instance. This attribute is used by Data Pipeline to
--   carry out the copy task from Amazon RDS to Amazon S3.</li>
--   <li><a>rdsdsSecurityGroupIds</a> - The security group IDs to be used
--   to access a VPC-based RDS DB instance. Ensure that there are
--   appropriate ingress rules set up to allow access to the RDS DB
--   instance. This attribute is used by Data Pipeline to carry out the
--   copy operation from Amazon RDS to an Amazon S3 task.</li>
--   </ul>
rdsDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec

-- | The Amazon S3 location of the <tt>DataSchema</tt> .
rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon RDS
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> Define your <tt>DataSchema</tt> as a
--   series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of
--   an Amazon RDS database.
rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase

-- | The query that is used to retrieve the observation data for the
--   <tt>DataSource</tt> .
rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text

-- | The AWS Identity and Access Management (IAM) credentials that are used
--   connect to the Amazon RDS database.
rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials

-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
--   from Amazon RDS using <tt>SelectSqlQuery</tt> is stored in this
--   location.
rdsdsS3StagingLocation :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon
--   Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
--   operation from Amazon RDS to an Amazon S3 task. For more information,
--   see <a>Role templates</a> for data pipelines.
rdsdsResourceRole :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rdsdsServiceRole :: Lens' RDSDataSpec Text

-- | The subnet ID to be used to access a VPC-based RDS DB instance. This
--   attribute is used by Data Pipeline to carry out the copy task from
--   Amazon RDS to Amazon S3.
rdsdsSubnetId :: Lens' RDSDataSpec Text

-- | The security group IDs to be used to access a VPC-based RDS DB
--   instance. Ensure that there are appropriate ingress rules set up to
--   allow access to the RDS DB instance. This attribute is used by Data
--   Pipeline to carry out the copy operation from Amazon RDS to an Amazon
--   S3 task.
rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text]

-- | The database details of an Amazon RDS database.
--   
--   <i>See:</i> <a>rdsDatabase</a> smart constructor.
data RDSDatabase

-- | Creates a value of <a>RDSDatabase</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdInstanceIdentifier</a> - The ID of an RDS DB instance.</li>
--   <li><a>rdsdDatabaseName</a> - Undocumented member.</li>
--   </ul>
rdsDatabase :: Text -> Text -> RDSDatabase

-- | The ID of an RDS DB instance.
rdsdInstanceIdentifier :: Lens' RDSDatabase Text

-- | Undocumented member.
rdsdDatabaseName :: Lens' RDSDatabase Text

-- | The database credentials to connect to a database on an RDS DB
--   instance.
--   
--   <i>See:</i> <a>rdsDatabaseCredentials</a> smart constructor.
data RDSDatabaseCredentials

-- | Creates a value of <a>RDSDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdcUsername</a> - Undocumented member.</li>
--   <li><a>rdsdcPassword</a> - Undocumented member.</li>
--   </ul>
rdsDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials

-- | Undocumented member.
rdsdcUsername :: Lens' RDSDatabaseCredentials Text

-- | Undocumented member.
rdsdcPassword :: Lens' RDSDatabaseCredentials Text

-- | The datasource details that are specific to Amazon RDS.
--   
--   <i>See:</i> <a>rdsMetadata</a> smart constructor.
data RDSMetadata

-- | Creates a value of <a>RDSMetadata</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rmSelectSqlQuery</a> - The SQL query that is supplied during
--   <tt>CreateDataSourceFromRDS</tt> . Returns only if <tt>Verbose</tt> is
--   true in <tt>GetDataSourceInput</tt> .</li>
--   <li><a>rmDataPipelineId</a> - The ID of the Data Pipeline instance
--   that is used to carry to copy data from Amazon RDS to Amazon S3. You
--   can use the ID to find details about the instance in the Data Pipeline
--   console.</li>
--   <li><a>rmDatabase</a> - The database details required to connect to an
--   Amazon RDS.</li>
--   <li><a>rmDatabaseUserName</a> - Undocumented member.</li>
--   <li><a>rmResourceRole</a> - The role (DataPipelineDefaultResourceRole)
--   assumed by an Amazon EC2 instance to carry out the copy task from
--   Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rmServiceRole</a> - The role (DataPipelineDefaultRole) assumed
--   by the Data Pipeline service to monitor the progress of the copy task
--   from Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   </ul>
rdsMetadata :: RDSMetadata

-- | The SQL query that is supplied during <tt>CreateDataSourceFromRDS</tt>
--   . Returns only if <tt>Verbose</tt> is true in
--   <tt>GetDataSourceInput</tt> .
rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text)

-- | The ID of the Data Pipeline instance that is used to carry to copy
--   data from Amazon RDS to Amazon S3. You can use the ID to find details
--   about the instance in the Data Pipeline console.
rmDataPipelineId :: Lens' RDSMetadata (Maybe Text)

-- | The database details required to connect to an Amazon RDS.
rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase)

-- | Undocumented member.
rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2
--   instance to carry out the copy task from Amazon RDS to Amazon S3. For
--   more information, see <a>Role templates</a> for data pipelines.
rmResourceRole :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultRole) assumed by the Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rmServiceRole :: Lens' RDSMetadata (Maybe Text)

-- | Describes the real-time endpoint information for an <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>realtimeEndpointInfo</a> smart constructor.
data RealtimeEndpointInfo

-- | Creates a value of <a>RealtimeEndpointInfo</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>reiCreatedAt</a> - The time that the request to create the
--   real-time endpoint for the <tt>MLModel</tt> was received. The time is
--   expressed in epoch time.</li>
--   <li><a>reiEndpointURL</a> - The URI that specifies where to send
--   real-time prediction requests for the <tt>MLModel</tt> .</li>
--   <li><a>reiEndpointStatus</a> - The current status of the real-time
--   endpoint for the <tt>MLModel</tt> . This element can have one of the
--   following values: * <tt>NONE</tt> - Endpoint does not exist or was
--   previously deleted. * <tt>READY</tt> - Endpoint is ready to be used
--   for real-time predictions. * <tt>UPDATING</tt> - Updating/creating the
--   endpoint.</li>
--   <li><a>reiPeakRequestsPerSecond</a> - The maximum processing rate for
--   the real-time endpoint for <tt>MLModel</tt> , measured in incoming
--   requests per second.</li>
--   </ul>
realtimeEndpointInfo :: RealtimeEndpointInfo

-- | The time that the request to create the real-time endpoint for the
--   <tt>MLModel</tt> was received. The time is expressed in epoch time.
reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)

-- | The URI that specifies where to send real-time prediction requests for
--   the <tt>MLModel</tt> .
reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text)

-- | The current status of the real-time endpoint for the <tt>MLModel</tt>
--   . This element can have one of the following values: * <tt>NONE</tt> -
--   Endpoint does not exist or was previously deleted. * <tt>READY</tt> -
--   Endpoint is ready to be used for real-time predictions. *
--   <tt>UPDATING</tt> - Updating/creating the endpoint.
reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)

-- | The maximum processing rate for the real-time endpoint for
--   <tt>MLModel</tt> , measured in incoming requests per second.
reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)

-- | Describes the data specification of an Amazon Redshift
--   <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>redshiftDataSpec</a> smart constructor.
data RedshiftDataSpec

-- | Creates a value of <a>RedshiftDataSpec</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rDataSchemaURI</a> - Describes the schema location for an
--   Amazon Redshift <tt>DataSource</tt> .</li>
--   <li><a>rDataSchema</a> - A JSON string that represents the schema for
--   an Amazon Redshift <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rDatabaseInformation</a> - Describes the <tt>DatabaseName</tt>
--   and <tt>ClusterIdentifier</tt> for an Amazon Redshift
--   <tt>DataSource</tt> .</li>
--   <li><a>rSelectSqlQuery</a> - Describes the SQL Query to execute on an
--   Amazon Redshift database for an Amazon Redshift <tt>DataSource</tt>
--   .</li>
--   <li><a>rDatabaseCredentials</a> - Describes AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon
--   Redshift database.</li>
--   <li><a>rS3StagingLocation</a> - Describes an Amazon S3 location to
--   store the result set of the <tt>SelectSqlQuery</tt> query.</li>
--   </ul>
redshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec

-- | Describes the schema location for an Amazon Redshift
--   <tt>DataSource</tt> .
rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon Redshift
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> . Define your <tt>DataSchema</tt> as
--   a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rDataSchema :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>ClusterIdentifier</tt> for
--   an Amazon Redshift <tt>DataSource</tt> .
rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase

-- | Describes the SQL Query to execute on an Amazon Redshift database for
--   an Amazon Redshift <tt>DataSource</tt> .
rSelectSqlQuery :: Lens' RedshiftDataSpec Text

-- | Describes AWS Identity and Access Management (IAM) credentials that
--   are used connect to the Amazon Redshift database.
rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials

-- | Describes an Amazon S3 location to store the result set of the
--   <tt>SelectSqlQuery</tt> query.
rS3StagingLocation :: Lens' RedshiftDataSpec Text

-- | Describes the database details required to connect to an Amazon
--   Redshift database.
--   
--   <i>See:</i> <a>redshiftDatabase</a> smart constructor.
data RedshiftDatabase

-- | Creates a value of <a>RedshiftDatabase</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdDatabaseName</a> - Undocumented member.</li>
--   <li><a>rdClusterIdentifier</a> - Undocumented member.</li>
--   </ul>
redshiftDatabase :: Text -> Text -> RedshiftDatabase

-- | Undocumented member.
rdDatabaseName :: Lens' RedshiftDatabase Text

-- | Undocumented member.
rdClusterIdentifier :: Lens' RedshiftDatabase Text

-- | Describes the database credentials for connecting to a database on an
--   Amazon Redshift cluster.
--   
--   <i>See:</i> <a>redshiftDatabaseCredentials</a> smart constructor.
data RedshiftDatabaseCredentials

-- | Creates a value of <a>RedshiftDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdcUsername</a> - Undocumented member.</li>
--   <li><a>rdcPassword</a> - Undocumented member.</li>
--   </ul>
redshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials

-- | Undocumented member.
rdcUsername :: Lens' RedshiftDatabaseCredentials Text

-- | Undocumented member.
rdcPassword :: Lens' RedshiftDatabaseCredentials Text

-- | Describes the <tt>DataSource</tt> details specific to Amazon Redshift.
--   
--   <i>See:</i> <a>redshiftMetadata</a> smart constructor.
data RedshiftMetadata

-- | Creates a value of <a>RedshiftMetadata</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>redSelectSqlQuery</a> - The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.</li>
--   <li><a>redRedshiftDatabase</a> - Undocumented member.</li>
--   <li><a>redDatabaseUserName</a> - Undocumented member.</li>
--   </ul>
redshiftMetadata :: RedshiftMetadata

-- | The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.
redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text)

-- | Undocumented member.
redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)

-- | Undocumented member.
redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text)

-- | Describes the data specification of a <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>s3DataSpec</a> smart constructor.
data S3DataSpec

-- | Creates a value of <a>S3DataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>sdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon S3 <tt>DataSource</tt> . The <tt>DataSchema</tt> defines
--   the structure of the observation data in the data file(s) referenced
--   in the <tt>DataSource</tt> . You must provide either the
--   <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>sdsDataSchemaLocationS3</a> - Describes the schema location in
--   Amazon S3. You must provide either the <tt>DataSchema</tt> or the
--   <tt>DataSchemaLocationS3</tt> .</li>
--   <li><a>sdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>sdsDataLocationS3</a> - The location of the data file(s) used
--   by a <tt>DataSource</tt> . The URI specifies a data file or an Amazon
--   Simple Storage Service (Amazon S3) directory or bucket containing data
--   files.</li>
--   </ul>
s3DataSpec :: Text -> S3DataSpec

-- | A JSON string that represents the schema for an Amazon S3
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . You must provide either the <tt>DataSchema</tt>
--   or the <tt>DataSchemaLocationS3</tt> . Define your <tt>DataSchema</tt>
--   as a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
sdsDataSchema :: Lens' S3DataSpec (Maybe Text)

-- | Describes the schema location in Amazon S3. You must provide either
--   the <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> .
sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text)

-- | The location of the data file(s) used by a <tt>DataSource</tt> . The
--   URI specifies a data file or an Amazon Simple Storage Service (Amazon
--   S3) directory or bucket containing data files.
sdsDataLocationS3 :: Lens' S3DataSpec Text

-- | A custom key-value pair associated with an ML object, such as an ML
--   model.
--   
--   <i>See:</i> <a>tag</a> smart constructor.
data Tag

-- | Creates a value of <a>Tag</a> with the minimum fields required to make
--   a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>tagValue</a> - An optional string, typically used to describe
--   or define the tag. Valid characters include Unicode letters, digits,
--   white space, _, ., /, =, +, -, %, and @.</li>
--   <li><a>tagKey</a> - A unique identifier for the tag. Valid characters
--   include Unicode letters, digits, white space, _, ., /, =, +, -, %, and
--   @.</li>
--   </ul>
tag :: Tag

-- | An optional string, typically used to describe or define the tag.
--   Valid characters include Unicode letters, digits, white space, _, .,
--   /, =, +, -, %, and @.
tagValue :: Lens' Tag (Maybe Text)

-- | A unique identifier for the tag. Valid characters include Unicode
--   letters, digits, white space, _, ., /, =, +, -, %, and @.
tagKey :: Lens' Tag (Maybe Text)
