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
pip install blazeeSign 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)
# 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.
