Instiq
Chapter 4 · ML Solution Monitoring, Maintenance, and Security·v2.1.0·Updated 6/14/2026·~9 min

What's changed: In-scope coverage: monitoring/ops/cost/security services

4.1Model Monitoring and Drift

Key points

Understand drift detection with SageMaker Model Monitor, endpoint monitoring with CloudWatch, and retraining triggered by drift. The starting point for "Monitoring, Maintenance, and Security" in MLA-C01.

Production models degrade over time (drift). Continuous monitoring and retraining when needed are key operations.

4.1.1Monitoring and drift detection

Diagram of three elements: Model Monitor (data/model drift vs. training baseline; quality/bias drift), CloudWatch (latency/errors/logs; alarms; endpoint metrics), and a retrain trigger (drift → retrain via Pipelines; keep the model fresh).
Monitoring models in production
  • Model Monitor: compares production data to the training baseline to detect data drift / model-quality drift / bias drift.
  • CloudWatch: monitors endpoint latency, error rate, invocations, and raises alarms.
  • Retraining: trigger retraining/redeployment via Pipelines on drift detection or schedule.
Exam point

Common on MLA: production accuracy degrades = data/model drift (detect with Model Monitor), detect → retrain (Pipelines), endpoint latency/error monitoring = CloudWatch. Drift comes from shifting data distributions.

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