This article covers what to monitor, performance and input distributions, the thresholds that should trigger review, and the change control that keeps a deployed system honest over time, so that drift is caught by design rather than discovered after an incident.
AI Risk & Governance
Continuous Monitoring: Catching Model Drift Before It Causes Harm
Deployment is not the finish line. As practice patterns, data, and equipment change, a model's performance can drift quietly until it fails. Monitoring is how you catch that in time.