SRE & Production Readiness

Chapter 5 of 6 — AI Cloud Engineer Roadmap

Everything through Chapter 4 gets a service deployed and shipping changes automatically. This chapter asks the question that actually matters once something is live: how do you know when it breaks, and how fast can you find out why?

What you'll build: a live incident simulation with real dashboards — metrics flowing into Grafana and Prometheus, alerts firing on the conditions that actually predict an outage, not just CPU usage.

Tools: Grafana, Prometheus

Where AI helps: AI generates a reasonable starting dashboard JSON and a list of common alert rules fast — you still own calibrating the thresholds (what's actually an incident versus normal noise) and the incident command process once an alert fires. A dashboard nobody trusts because it cries wolf is worse than no dashboard.

Modules in this chapter

• APM vs Observability — what each actually measures, and why "I have logs" isn't observability
• Introduction to Observability — the three pillars: metrics, logs, traces

Why this matters

The gap between "it works on my deploy" and "it's production-ready" is almost entirely observability and incident response — not more features. Companies don't lose trust because a service has a bug; they lose trust because nobody noticed the bug for six hours. This chapter is where you build the muscle of noticing first.

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Next: AI, LLM & Agents

Chapter 6 is the capstone: a text-to-SQL RAG agent, deployed onto the Kubernetes cluster and observability stack you built in the chapters before it.