Feature Stores: Feast and Tecton
Online vs offline feature stores, feature versioning, point-in-time joins, serving latency.
The Feature Store Problem
Teams repeatedly re-implement the same features, and training pipelines compute them differently from serving code. A feature store centralizes feature definitions, reuse, and serving, eliminating duplicate work and training-serving skew.
Feast and Tecton
Two leading feature stores:
- Feast: open-source, lightweight, brings your own infrastructure
- Tecton: commercial, managed, adds streaming feature pipelines and governance
Both share the core concepts of entities, features, and offline/online stores.
All lessons in this course
- AI System Architecture Patterns
- Scalable ML Pipelines with Airflow
- Feature Stores: Feast and Tecton
- AI System Observability and Monitoring