Metadata-Version: 2.1
Name: blazee
Version: 0.1.5
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
Description: # 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
        - XGBoost
        - LightGBM
        
        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.
        
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
