Production Observability & Monitoring

Every production service — a payments API, a batch ETL job, a recommendation model, a login endpoint — asks the same three questions when something feels wrong: is it broken, where is it broken, and why is it broken? Observability is the discipline of instrumenting your systems so those three questions have fast, reliable answers, before a customer files a support ticket that becomes your first signal. This is infrastructure work, not ML work — it applies exactly as much to a stateless checkout service as it does to a model-serving endpoint — and getting it right is a prerequisite for running anything in production responsibly, ML included.

A boundary worth stating up front, because it is easy to blur: this subject is the general observability stack — system health, service-level instrumentation, dashboards, and the SLO/alerting mechanics that apply to any service, whether or not it involves a model at all. The ML Monitoring, Drift & Retraining subject owns the ML-specific statistical layer — PSI, KL/KS divergence, prediction-distribution drift math, delayed-label handling, and retraining triggers — in real depth, with worked calculations. Here, prediction-distribution health and feature freshness get one bullet each, as a pointer to where that depth lives, and no more; if you find yourself wanting to compute a PSI value or design a retraining policy, you are looking for the other subject. Think of this subject as the floor everything else in the MLOps track stands on: the logging, metrics, tracing, dashboarding and alerting mechanics that a canary release (Model Release Strategies: Canary, Shadow & Rollback), a drift alert (ML Monitoring, Drift & Retraining), and an incident response all depend on existing already.