Zuletzt aktualisiert am:
Continuous tracking of an AI model’s performance, data drift, and operational metrics to detect degradation or emerging risks.
Live observability pipelines collect metrics - accuracy, latency, input-distribution drift, fairness KPIs, error rates - and compare them against baseline thresholds. Automated alerts trigger when anomalies occur. Governance defines what to monitor, alert thresholds, escalation procedures, and retraining triggers, and logs all monitoring data for audit purposes.
Praxisbeispiel:
An e-commerce recommendation model tracks click-through rates and user-demographic lift daily. When CTR falls more than 5% or demographic lift diverges, an alert is sent to the Data Science Ops team, who investigate data-pipeline issues or initiate model retraining to restore performance.
