Continuous tracking of AI system performance, data drift, bias metrics, and security events to detect and address emerging risks over time.
Encompasses model-monitoring (accuracy, drift), data-pipeline health (ingestion failures), fairness assessments (demographic disparity), and security alerts (intrusion detections). Ongoing monitoring uses dashboards, automated alerts, and periodic reviews. Governance mandates monitoring coverage requirements, threshold definitions, incident-response playbooks, and regular reporting to oversight bodies.
An e-commerce platform monitors its recommendation engine daily for accuracy drop, user-segment bias, and runtime errors. Custom dashboards display all metrics; when any metric crosses its threshold, the ML Ops team receives a Slack alert and follows a predefined incident-response protocol to investigate and remediate.
