Loading...
Loading...
Browse 4 real-world technical and behavioral interview questions about Training serving skew. Review scenarios, edge cases, and architectural best practices.
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.
Re-pointing traffic at the previous version is only safe if nothing else moved with it. The feature transformations, the input schema and any consumer calibrated to the current score distribution may have changed, so the unit of rollback is the model plus its features and its contract.
Check whether training and serving compute the same features from the same definitions, because the usual cause is two implementations that disagree slightly. Nothing raises an error when they diverge - the model receives plausible numbers that mean something different from the ones it learned on.
Features must be computable at decision time, training and serving must share one transformation implementation, the model and dataset version must be pinned together, drift and realised performance monitored separately because labels arrive late, and rollback must move model and features as one unit.