Performance & Drift Monitoring

Deploying an AI model is not the end of the AI lifecycle. Organizations must continuously monitor AI systems to ensure performance remains reliable, accurate, and aligned with business objectives. Over time, changing data, evolving environments, and shifting user behavior can cause model performance to degrade. In this lesson, learners will explore performance monitoring, model drift, operational metrics, alerting mechanisms, governance oversight, and assurance practices used to maintain trustworthy AI operations. Understanding these concepts helps organizations detect emerging issues early, improve resilience, and support ongoing confidence in AI-driven outcomes.