Chapter 8: Monitoring & Observability

Monitoring and observability are the disciplines that let you understand what your production systems are doing, detect when things go wrong, and diagnose why. Without observability, operating a production ML system is like driving a car at night with the headlights off — you might stay on the road for a while, but eventually you will crash.

The three pillars of observability — logs, metrics, and traces — each capture different dimensions of system behavior. Logs record individual events with rich context. Metrics track aggregated numerical measurements over time. Traces follow individual requests across distributed services. Together, they provide complete visibility into your system's health and behavior.

This chapter covers the full observability stack: from writing structured logs and instrumenting Prometheus metrics, through distributed tracing with OpenTelemetry, to building alert systems that wake engineers for real problems (not false positives). It also covers the human side: how to define reliability targets (SLOs), secure your APIs, and respond to incidents effectively. The goal is not just detecting problems but building systems and processes that minimize their impact and prevent recurrence.