What is Feature Store?
Feature Store — A centralized repository for storing, managing, and serving features used in machine learning models.
A feature store is a centralized repository for storing, managing, and serving ML features. It ensures consistency between training and inference, prevents feature duplication across teams, and provides point-in-time correctness for historical feature values.
Frequently Asked Questions
When do I need a feature store?
When multiple teams reuse the same features, when you need to ensure training/serving consistency, or when feature computation is expensive and should be cached rather than recomputed.
What feature stores are available?
Feast (open source), Tecton, Databricks Feature Store, AWS SageMaker Feature Store, and Hopsworks. Each offers different trade-offs for scale, latency, and managed vs. self-hosted.
Can I start without a feature store?
Yes. For early projects, simple feature computation in code is fine. A feature store becomes valuable when you have multiple models, teams, or production serving requirements.