Operating, Monitoring, and Securing ML and AI Solutions

A service that continuously monitors deployed models for data quality, model quality, bias drift, and feature attribution drift by comparing live data against a baseline, emitting CloudWatch metrics and reports.

Data quality, model quality, bias drift, and feature attribution drift.

Data Capture on the endpoint, which logs a sampled fraction of inference request/response payloads to S3 for the monitoring jobs to analyze.

You create a monitoring schedule (hourly/daily cron) that runs a processing job to compare captured data against the baseline and report violations to S3 and CloudWatch.

Data quality monitoring checks input feature distributions/schema against a baseline; model quality monitoring checks prediction accuracy against ground-truth labels.

Violation reports go to S3 and metrics to CloudWatch; create CloudWatch alarms on those metrics to notify (SNS) or trigger retraining (EventBridge).