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))

The following commands will start Feast in online-only mode.

git clone https://github.com/gojek/feast.git
cd feast/infra/docker-compose
cp .env.sample .env
docker-compose up -d

A Jupyter Notebook is now available to start using Feast.

Please see the links below to set up Feast for batch/historical serving with BigQuery.

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

• Why Feast?
• Concepts
• Installation
• Examples
• Roadmap
• Change Log
• Slack (#Feast)

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.