Skip to content

Yanson/feast

 
 

Repository files navigation

Feast - Feature Store for Machine Learning

Overview

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))

Getting Started with Docker Compose

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.

Important resources

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

Notice

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.

About

Feature Store for Machine Learning

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Java 61.2%
  • Python 33.4%
  • Shell 2.3%
  • Go 1.4%
  • TSQL 0.5%
  • Makefile 0.5%
  • Other 0.7%