Guides ยท Science
ML Model Monitoring Basics
Monitor ML models in production
This guide covers monitoring ML models: log predictions and inputs, watch performance metrics, detect data/label drift, and set alerts with retraining triggers.
- ml monitoring
- drift
- performance
- alerts
- retraining
Log key signals
Capture inputs, predictions, and outcomes where possible.
Track performance
Monitor accuracy/recall or regression error over time by segment.
Detect drift
Watch input distributions and concept drift; set thresholds for investigation.
Plan retraining
Define triggers for retraining or rollback and review regularly.