Feature Stores
Offline-online parity, point-in-time correctness, materialisation, and the failure they exist to prevent.
6concepts
71flashcards
44minutes of reading
- 01 Embeddings as Managed Features Why an embedding stored in a feature store is only meaningful alongside the exact encoder that produced it, what an encoder upgrade costs in backfill and downstream retraining, and how backward-compatible training and learned alignment trade model quality for avoiding that cost.
- 02 Offline-Online Parity and Training-Serving Skew Why the same feature computed by two pipelines is rarely the same number, the categories of divergence, and the architectural choices that eliminate rather than manage the problem.
- 03 Point-in-Time Correctness The join that a feature store exists to get right, why a naive join on entity ID leaks the future into training labels, and what an as-of join costs to compute.
- 04 Streaming Aggregations for Real-Time Features How to compute a sliding-window count over millions of entities within a serving latency budget, why exact windows are usually unaffordable, and the approximations that are safe.