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AI & Machine Learning · Intermediate

Model Monitoring

Continuously tracking a deployed model's inputs, outputs, performance and fairness to detect degradation.

Definition

Monitoring watches data quality, data drift, concept drift, prediction distributions, bias drift, latency and errors, alerting or triggering retraining when thresholds are breached. Services include SageMaker Model Monitor, Vertex AI Model Monitoring and Azure ML model monitoring.

Exam tip

Monitoring should trigger alerts or retraining when drift or quality thresholds are breached.

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