Amazon SageMaker Model Monitor enables real-time monitoring for deployed ML models to detect issues such as data quality problems and concept drift. This setup will help you implement Model Monitor correctly and provide insights into common troubleshooting steps.
- AWS Account with necessary permissions to use Amazon SageMaker and Model Monitor.
- SageMaker Environment configured to work with deployed ML models.
- Baseline Statistics from your training data (used to detect drift).
Model Monitor collects and analyzes data from deployed models, identifying data quality issues and anomalies without requiring manual intervention.
Detects concept drift by comparing live data with baseline statistics from the training data, alerting you when significant changes are detected that may impact predictive accuracy.
Scales with the volume of your models and data. Customize monitoring thresholds, schedules, and alert triggers based on specific deployment needs.
Performs statistical tests on production data and sends alerts for significant deviations, enabling proactive maintenance and retraining.