Observability & AIOps
NickPinaAbout This Course
A hands-on module for infrastructure engineers who already run Kubernetes and now need to operate AI workloads with confidence. It starts from monitoring you know — metrics, logs, dashboards — and builds toward observability as a practice: OpenTelemetry collection architecture, RED/USE and the golden signals, SLIs, SLOs, error budgets, and multi-window burn-rate alerting.
From there it covers what AIOps genuinely delivers versus vendor promises: anomaly detection without alert storms, event correlation and topology, policy-based remediation with guardrails, and AI copilots for operations. The final chapters turn the lens on AI itself — model and data observability, tracing LLM applications, and a capstone where you diagnose a degraded LLM service end to end.
Four chapters, four graded knowledge checks. Written for engineers coming from the MLOps and Model Serving modules of this roadmap.