[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODAxNTM","title","AI for Network Engineers","state","published","contactAuthorMethod","email","aboutText","

AI is already changing network operations — this book shows you how to use it without breaking your network.

Most \"AI for engineers\" material is either hype or toy examples.

AI for Network Engineers is different: it's a practical, code‑first playbook written for people who actually run networks.

Every concept is tied to something you can run, and the whole book builds on three disciplines that keep AI safe and useful in production — ground the model in real data, let it propose while a deterministic check disposes, and never mistake a confident tone for correctness.

You'll start by making your first AI call against a real log line and finish by deploying three complete tools: a syslog triage bot, a fleet‑wide config compliance checker, and an interactive troubleshooting assistant. Along the way you'll learn how to generate and validate configuration, turn thousands of log lines into a ranked summary, build a private \"ask your network\" assistant with RAG, and create bounded, read‑only agents you can actually trust near production.

Examples mix Cisco IOS/NX‑OS and Juniper Junos, because real networks are multi‑vendor — and every tool is written so you can point it at a cloud API or a local model by changing a single line, keeping sensitive configs inside your perimeter.

What you'll learn:

Run AI from Python against real device data — cloud or local — with one reusable client

Summarize, triage, and root‑cause logs; generate and audit configs against your own standard

Get reliable structured (JSON) output, count tokens and control cost, and harden API calls for production

Build RAG over your own configs and docs, and design safe, allow‑listed agents

Choose and self‑host local models, and put proper security, privacy, and governance in place

Ship three real, deployable projects end to end

Who it's for: working network engineers, NetDevOps practitioners, and NOC teams who are comfortable at the CLI and can read Python, but aren't AI specialists.

Inside: 19 chapters across 5 parts, ~165 pages, runnable Python throughout, and a prompt cookbook you'll keep coming back to.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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