Blazee makes it easy to deploy Machine Learning models on the cloud and turn them into an awesome prediction API.
Project 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 only support supervised learning Scikit Learn models and pipeline.
Coming soon:
- H2O
- Keras
- Tensorflow
- Scikit Learn Unsupervised learning models
Installation
Install from pip
pip install blazee
Sign up and get an API Key from https://blazee.io
Usage
# 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)
Support
Contact us at support@blazee.io or open a Github Issue for any question or bug report.
Project details
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