Metadata-Version: 2.1
Name: blazee
Version: 0.1.4
Summary: Blazee makes it easy to deploy Machine Learning models on the cloud and turn them into an awesome prediction API.
Home-page: https://github.com/blazee-io/blazee-python
Author: blazee.io
Author-email: support@blazee.io
License: GNU General Public License v3.0
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: python-dateutil
Requires-Dist: requests

# Python Library for Blazee

## Intro

Blazee is the easiest and fastest way to turn your Machine Learning
models and pipelines into a production ready prediction API.

It allows you to deploy trained models straight from a Jupyter Notebook
or any other model training environment, and access them live from anywhere
using the Blazee HTTP API.

This library can also be used

## Supported Frameworks

At the moment, we support the following frameworks:

- Scikit Learn (Supervised learning models and pipeline)
- Keras
- PyTorch

Coming soon:

- H2O
- XGBoost
- LightGBM
- Tensorflow

## Installation

Install from pip

```shell
pip install blazee
```

Sign up and get an API Key from https://blazee.io

## Usage

```python
# Train your model like you usually do
>>> from sklearn.linear_model import LogisticRegressionCV
>>> clf = LogisticRegressionCV()
>>> ...
>>> clf.train(X)

# Deploy your model on Blazee
# Get your API Key on https://blazee.io
>>> from blazee import Blazee
>>> bz = Blazee(YOUR_API_KEY)
>>> model = bz.deploy_model(clf)
Uploading model to Blazee...
Successfully deployed model bdea76f4-fa0f-4ef1-8bc5-f36978a4488e
Deploying model... This will take a few moments

# Predict a single sample
>>> pred = model.predict(X[0])
>>> pred.prediction
1
>>> pred.probas
{0: 0.08, 1:0.91, 2: 0.01}

# Or predict a batch
>>> preds = model.batch_predict(X)

# Deploy another version of the model
>>> clf2 = SGDClassifier()
>>> ...
>>> clf2.train(X)
>>> model.update(clf2)
```

## Support

Contact us at support@blazee.io or open a Github Issue for any question or bug report.


