Monitoring & Observability

This lesson introduces monitoring and observability practices used to maintain AI systems after deployment. Learners explore performance monitoring, quality monitoring, drift detection, alerting, and operational response processes. The lesson explains how organizations track system health, detect degradation before it impacts users, and respond to incidents efficiently. Students will learn why monitoring is a critical component of trustworthy AI operations and how observability provides the visibility required to manage AI systems at scale.