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Model Monitoring is the ongoing observation of a deployed model's inputs, outputs, and performance to detect degradation or drift over time.
Model Monitoring, the ongoing observation of a deployed model's inputs, outputs, and performance to detect degradation or drift over time.
A model that performed well at launch can quietly decay as the world changes around it. Model monitoring watches live performance, data distributions, and error patterns so that drift, bias creep, or failures are caught and corrected. It is the operational backbone of post-deployment governance and a core part of MLOps.
Source: NIST AI RMF (Manage function); MLOps practice
A model that performed well at launch can quietly decay as the world changes around it. Model monitoring watches live performance, data distributions, and error patterns so that drift, bias creep, or failures are caught and corrected. It is the operational backbone of post-deployment governance and a core part of MLOps.
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