Machine Learning Feature Stores

Feast is a machine learning feature store and MLOps data infrastructure layer. It provides a centralized system for managing and serving features across offline training and online production environments, utilizing an online feature serving layer for low-latency retrieval. The project centers on a feature registry that acts as a central catalog for defining, governing, and discovering feature services. It employs a unified data access layer to decouple feature retrieval from physical storage and includes a point-in-time data generator to create historically accurate training datasets that prevent data leakage. The platform covers a broad range of capabilities including real-time model inference, streaming data feature engineering, and the generation of training datasets. It also supports vector embedding search for similarity-based retrieval and feature quality validation to maintain data integrity.

Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates materialization pipelines that move batch features from offline stores to online stores using configurable compute engines. Feast distinguishes itself through its multi-protocol serving surface, exposing the same feature values simultaneously via REST, gRPC, and MCP protocols to support diverse client ecosystems including AI agents. It includes an on-demand transformation framework that applies Python-based feature transformations at retrieval time, combining precomputed features with request-time data for flexible serving. The project also provides entity-key collocated storage, storing all features for a single entity in one document to reduce online reads to a single lookup per request, and a background registry cache refresh that prevents serving requests from blocking on cache updates. The platform covers the full lifecycle of feature management, including feature engineering and transformation from batch and streaming sources, governance and access control with application-level RBAC and OIDC authentication, real-time inference serving, and historical feature retrieval for training. It supports vector search and retrieval-augmented generation workflows by storing and querying embeddings for similarity search. Feast integrates with a wide range of storage backends, compute engines, and data sources, and provides tooling for deployment on Kubernetes, monitoring with Prometheus and OpenTelemetry, and lineage tracking with OpenLineage.