i4late / feast

Feature Store for Machine Learning

Overview

Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Please see our documentation for the motivation behind the project.

Getting Started with Docker Compose

Clone the latest stable version of the Feast repository and navigate to the infra/docker-compose sub-directory:

git clone --depth 1 --branch v0.7.0 https://github.com/feast-dev/feast.git
cd feast/infra/docker-compose
cp .env.sample .env

The .env file can optionally be configured based on your environment.

Bring up Feast:

docker-compose up -d

The command above will bring up a complete Feast deployment with a Jupyter Notebook containing example notebooks.

Important resources

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

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

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.