AI Monitoring and Observability
For engineers and ML practitioners who run models or LLM features in production and need to know when they quietly go wrong. You will be able to instrument a service with logs, metrics and traces, detect drift and anomalies with sound statistics, and build alerts and retraining gates that a team can trust.
Course Content
Learn why AI systems fail silently and how to catch it: the signals worth collecting, how logs, metrics and traces fit together, and how to detect drift and anomalies without drowning in false alarms. 3 lessons, about 55 minutes.
Choose and wire up the tools that turn telemetry into answers: metrics, logs and trace stores, LLM-specific platforms, tracing pipelines that hold up under load, and alerts people still read. 3 lessons, about 50 minutes.
Build a local observability stack for a mock LLM API that catches a silent quality drop, routes it to the right channel and refuses to retrain on bad data. 1 lesson, about 20 minutes.