Continuous Training and Feature Parity

Fresh data can still be wrong

A scheduled training job should validate its inputs before fitting a model. A schema can be unchanged while a source region disappears or labels become incomplete. Keep data and label maturity checks separate from training success.

Record the prediction timestamp, feature availability time, and label availability time. Train and evaluate using information that would have existed at the decision boundary.

A feature store can help share feature definitions and retrieve historical values at the right time. It does not make a source correct or remove the need for leakage checks. Compare it with a versioned SQL table and batch export before adding another service; choose it when reuse, freshness, and point-in-time retrieval justify the operating cost.