Feature Store¶
A feature store makes model inputs reproducible across training and serving while preserving event-time correctness, freshness, and ownership.
flowchart LR
J["Junior: features and stores"] --> M["Middle: build and retrieve"]
M --> S["Senior: correctness and reliability"]
S --> P["Professional: platform internals and operations"]
flowchart TD
SRC["Data sources"] --> TRANSFORM["Feature transformations"]
TRANSFORM --> OFFLINE[("Offline store")]
OFFLINE --> TRAIN["Point-in-time training data"]
TRANSFORM --> MATERIALIZE["Materialization"]
MATERIALIZE --> ONLINE[("Online store")]
ONLINE --> PREDICT["Online prediction"]
Levels¶
| Level | Guide | You are done when... |
|---|---|---|
| Junior | junior.md | You can explain features, offline/online stores, and training-serving skew |
| Middle | middle.md | You can define entities/features and retrieve correct historical and online values |
| Senior | senior.md | You can design point-in-time joins, materialization, freshness, backfills, and fallbacks |
| Professional | professional.md | You can operate a multi-tenant feature platform with strong correctness and lifecycle controls |
Practice rule¶
Define feature semantics and event-time behavior before choosing storage. A fast lookup of the wrong historical value is still incorrect.