Deploying an AI application is not the finish line. Once the system is running in production, the real question becomes: Is it working as expected?

Models may slow down, infrastructure can fail, and predictions may gradually degrade as real-world data changes. Without proper visibility into the system, these issues can go unnoticed until users start experiencing problems.

Monitoring and observability provide the tools needed to understand what is happening inside a running AI system. Monitoring tracks key metrics such as latency, error rates, throughput, and resource usage. Observability goes deeper by helping engineers investigate why something went wrong through logs, traces, and detailed system insights.

In this chapter, you will learn how monitoring and observability work in modern AI systems.