Project Url: gojek/feast
Introduction: Feature Store for Machine Learning
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Feast (Feature Store) is a tool for managing and serving machine learning features. Feast is the bridge between models and data.

Feast aims to:

  • Provide a unified means of managing feature data from a single person to large enterprises.
  • Provide scalable and performant access to feature data when training and serving models.
  • Provide consistent and point-in-time correct access to feature data.
  • Enable discovery, documentation, and insights into your features.

TL;DR: Feast decouples feature engineering from feature usage. Features that are added to Feast become available immediately for training and serving. Models can retrieve the same features used in training from a low latency online store in production. This means that new ML projects start with a process of feature selection from a catalog instead of having to do feature engineering from scratch.

# Setting things up
fs = feast.Client('feast.example.com')
customer_features = ['CreditScore', 'Balance', 'Age', 'NumOfProducts', 'IsActive']

# Training your model (typically from a notebook or pipeline)
data = fs.get_batch_features(customer_features, customer_entities)
my_model = ml.fit(data)

# Serving predictions (when serving the model in production)
prediction = my_model.predict(fs.get_online_features(customer_features, customer_entities))

Important resources

Please refer to the official docs at https://feast.dev


Feast is a community project and is still under active development. Your feedback and contributions are important to us. Please have a look at our contributing guide for details.

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