jpugliesi / 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 more information about 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 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 pull && docker-compose up -d

Please wait for the containers to start up. This could take a few minutes since the quickstart contains demo infastructure like Kafka and Jupyter.

Once the containers are all running, please connect to the provided Jupyter Notebook containing example notebooks to try out.

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.

Contributors ✨

Thanks goes to these incredible people: