Feature Store — Sharing ML Features Across Teams

Three ML teams, three copies of the same feature engineering code. Each calculates “average customer spend over 90 days” slightly differently. A Feature Store solves this chaos.

What Is a Feature Store?¶

A central repository of ML features — preprocessed data attributes usable for both training and serving. Two modes:

• Offline — historical data for training (Snowflake/S3)
• Online — real-time features for inference (Redis/DynamoDB)

Feast as the Solution¶

Feast (Feature Store) — open-source, lightweight, integrates with our stack. Feature definitions as code in Git, materialization to the online store via Airflow.

Results¶

Consistent features across training and serving (no training-serving skew). Feature sharing across teams. Faster onboarding of new ML projects — “what features do we have?” is now a one-minute question, not a day-long one.

Feature Store = DRY Principle for ML¶

Don’t repeat feature engineering. Centralize, version, share.