Table of contents Introduction Quickstart Getting started Install Feast Create a feature repository Deploy a feature store Build a training dataset Load data into the online store Read features from the online store Community Roadmap Changelog Tutorials Driver Ranking Fraud Detection Concepts Overview Feature view Data model Online store Offline store Provider Architecture Feature service How-to Guides Running Feast in production Reference Data sources File BigQuery Redshift Offline stores File BigQuery Redshift Online stores SQLite Redis Datastore DynamoDB Providers Local Google Cloud Platform Amazon Web Services Feature repository feature_store.yaml .feastignore Feast CLI reference Python API reference Usage Contributing Contribution process Development guide Versioning policy Release process Feast on Kubernetes Getting started Install Feast Docker Compose Kubernetes (with Helm) Amazon EKS (with Terraform) Azure AKS (with Helm) Azure AKS (with Terraform) Google Cloud GKE (with Terraform) IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize) Connect to Feast Python SDK Feast CLI Learn Feast Concepts Overview Architecture Entities Sources Feature Tables Stores Tutorials Minimal Ride Hailing Example User guide Overview Getting online features Getting training features Define and ingest features Extending Feast Reference Configuration Reference Feast and Spark Metrics Reference Limitations API Reference Go SDK Java SDK Core gRPC API Python SDK Serving gRPC API gRPC Types Online Store Format Spec Offline Store Format Spec Advanced Troubleshooting Metrics Audit Logging Security Upgrading Feast