For Heads-of · Practitioner
Model Monitoring
Continuous observation of a deployed model inputs, outputs, performance metrics, and behaviour to detect degradation, drift, misuse, or security anomalies.
- monitoring
- observability
- drift
- performance
Metrics
Model accuracy/F1 score vs ground truth, data distributions, inference latency, error rates by demographic group, and anomaly flags.
Tools
Logging prediction data, comparing to baseline distributions, setting performance thresholds, tracking fairness metrics, and alerting on threshold breaches.
Triggers
Monitoring results should trigger investigation (Why did accuracy drop?) and potential action (Retrain? Rollback? Escalate?).