AIOps Overview
This section is for engineers who need to apply AI capabilities in operations: triage, observability, runbooks, and safe rollout—not train foundation models or deep-dive ML theory.
By the end of this section, you should be able to choose a learning path, name concrete tools and patterns, and know where to start hands-on.
Who this is for
Section titled “Who this is for”- SREs, platform engineers, and DevOps practitioners building or adopting AI-assisted workflows.
- Anyone who must evaluate LLM-generated scripts, runbooks, or diagnostics before they touch production.
Prerequisites
Section titled “Prerequisites”Comfort with baseline operations is assumed:
- Observability — metrics, logs, traces, alerting.
- CI/CD and GitOps — how changes reach clusters.
- Helm, operators, and GitOps — how packaging and delivery relate to LLM-assisted workflows.
Reading paths
Section titled “Reading paths”Path A — Observability first
- Intelligent Observability and AIOps
- LLM Diagnostics and Intelligent Runbooks
- RAG for Incident Operations
- Evaluating LLM Outputs in Operations
Path B — Hands-on stack and rollout
What to de-prioritize
Section titled “What to de-prioritize”You do not need full model training, MLOps pipelines, or graduate-level ML. The bar is: architect AI-assisted ops workflows, evaluate outputs, and lead safe adoption.
Topics in this section
Section titled “Topics in this section”| Topic | What you’ll get |
|---|---|
| Intelligent Observability | Anomaly detection, baselines, correlation, AI-assisted RCA |
| LLM Diagnostics & Runbooks | AI assistants, intelligent runbooks, log/trace interpretation |
| RAG for Incident Operations | Grounding LLMs with runbooks and incident history |
| Evaluating LLM Outputs | Hallucination risk, human-in-the-loop, prompt regression |
| Tooling and Stack | Platforms, Python libraries, vector stores, eval tools |
| Adoption Roadmap | Toil mapping, ROI, experimentation culture |
| 60-Day Plan | Structured upskilling with exercises |