Offline and online parity, point-in-time correctness, backfills, label collection and delay, and whether a feature store is worth its operational cost.
3 questions in MLOps & ML Platform.
It earns its cost when several models share features and when you need offline and online parity plus feature history for correct backfills. It does not when one team runs one model, because you have added a stateful service to the request path for benefits nobody is drawing on yet.
Label latency caps your retraining cadence at roughly the label delay, forces evaluation to run on a lagging window that is always partially incomplete, and pushes you towards proxy labels that arrive sooner. Proxies buy speed at the price of optimising something adjacent to the outcome you care about.
A training row must contain only feature values that were knowable at its own event timestamp. A naive equality join on entity id attaches whatever the feature table holds now, including values derived from after the event, which leaks the future and produces offline metrics that collapse in production.