AdvancedSystem-Design
45 min
Design an ML Feature Store
MLDataConsistencyStreaming
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Interview Question
Design an ML feature store that supports offline feature engineering and online low-latency serving with consistency guarantees.
Key Points to Cover
- Feature registry & schema/versioning; lineage and governance
- Offline pipeline (batch) and online pipeline (stream) materialization
- Consistency: point-in-time correctness, training/serving skew reduction
- Storage: offline (data lake) vs online (KV/Redis) with TTL/backfills
- Serving APIs, caching, and multi-tenant quotas/SLA
- Monitoring: feature drift, nulls, and freshness
Evaluation Rubric
Clear registry & versioning strategy25% weight
Correctness and skew mitigation25% weight
Hot/cold storage & materialization25% weight
Serving SLAs and monitoring25% weight
Hints
- 💡Point-in-time joins are essential for correctness.
Potential Follow-up Questions
- ❓How do you deprecate a feature safely?
- ❓How do you backfill online features?
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