📋 Model Monitor Cheat Sheet

Monitoring, maintenance, and security make up 24% of MLA-C01, and drift detection is a frequent production ML scenario.

Monitoring Types

  • Data quality monitoring compares incoming data to a baseline schema and distribution.
  • Model quality monitoring compares predictions to ground truth labels when available.
  • Bias drift checks whether bias metrics change over time.
  • Explainability drift checks whether feature attribution changes over time.

Operational Pattern

  • Create a baseline from representative data.
  • Capture production inference data.
  • Schedule monitoring jobs.
  • Send violations to CloudWatch and trigger review or retraining.

Practice Model Monitor Questions

Put your knowledge to the test with practice questions.

More MLA-C01 Cheat Sheets