No matter how well an AI system is designed, production incidents are inevitable. Services can fail, models can produce unexpected outputs, infrastructure can become overloaded, and data pipelines can break. What separates reliable systems from fragile ones is not the absence of incidents, but how quickly and effectively they are detected, diagnosed, and resolved.

AI applications add another layer of complexity. Incidents may involve model degradation, data quality issues, feature pipeline failures, or unexpected shifts in user behavior. Troubleshooting these problems often requires understanding both software systems and machine learning behavior.

In this chapter, you will learn how production incidents are handled in real-world AI systems.