Scheduled retraining or triggered retraining?

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Answer

$Scheduled is the sane default because it is predictable, testable, and forces the retraining path to stay working, which is the failure nobody notices until an emergency.$
$Its weakness is that it retrains when nothing changed, wasting compute, and does not react between runs.$
$Triggered retraining on a drift or performance signal reacts faster but introduces a nasty risk: an automated retrain on corrupted data deploys the corruption, so a trigger must be paired with data validation and an evaluation gate that can refuse to promote.$
$In practice run both, a regular cadence plus a trigger for material degradation, and always gate promotion on an offline evaluation against a fixed benchmark rather than promoting whatever the pipeline produced.$

Check yourself — multiple choice

Always retrain on every new batch
Schedule as the default since it keeps the path exercised, add a degradation trigger, and gate promotion on data validation plus offline evaluation so corrupted data cannot auto-deploy
Never retrain automatically
Trigger only, schedules waste compute

A cadence keeps the pipeline healthy; triggers add reactivity, and both need a promotion gate.

#mlops#monitoring#deployment

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