Design a feature store for a company running dozens of ML models in production. What problems does it solve, what are its core components, and what are the consistency and freshness challenges you'd need to address?
formulate your answer, then —
tldr
A feature store solves training-serving skew, feature duplication, and discovery by centralizing feature definitions, computation, and serving. The hard engineering problems are point-in-time correctness for training and consistency between the offline (batch) and online (real-time) stores — both require careful architectural choices around how and when features are materialized.
follow-up
- How would you handle a feature that's computed from another feature (derived features)? What are the dependency management challenges?
- Walk me through how you'd implement point-in-time correct joins in BigQuery for model training data creation.
- How would you design the alerting system for a feature store to catch data quality issues before they silently degrade model performance?