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Model monitoring
Model monitoring is continuously tracking a deployed model's inputs, outputs and performance over time, to catch drift or degradation before it causes serious harm.
Prerequisite: Data drift
What matters
Because concept drift and data drift are invisible unless actively watched for, monitoring is what turns a silent slow decline into a visible, actionable signal that retraining is needed.
Example
A monitoring dashboard might alert a team when a fraud model's precision on recent transactions drops noticeably below its historical baseline.
Common misconception
Model monitoring is not a one-time deployment checklist item; it needs to run continuously for as long as the model stays in production.
Recall question
Why does monitoring need to run continuously rather than just once at deployment?
Keywords and related concepts
model monitoringproduction MLdrift detectionconcept drift