Design concept drift detection with automated retraining safeguards
WHAT IT TESTS: MLOps design separating drift detection, triggers, and stability controls. ANSWER OUTLINE: Baseline monitors raise CloudWatch alarms; EventBridge triggers retraining with cooldowns; model registry gates promotion.
WHAT IT TESTS: Whether you can design production ML systems that detect concept drift and automate retraining safely. ANSWER OUTLINE: Establish baseline statistics and schedule Model Monitor to emit metrics to CloudWatch; use threshold alarms and cooldowns to trigger SageMaker Pipelines via EventBridge; require Model Registry approval and canary validation before promotion. RED FLAG: Suggesting retraining loops without human gates, data quality checks, or cost-aware throttling.
Read the original → aws.amazon.com
- #mlops
- #concept-drift
- #sagemaker
- #model-monitoring
- #automation
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