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AI for Network Engineers Automation, Troubleshooting & Ops

Name: AI for Network Engineers Automation, Troubleshooting &amp; Ops
Author: Baturi

Free Download AI for Network Engineers Automation, Troubleshooting & Ops

Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With AI for Network Engineers Automation, Troubleshooting & Ops, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 8/2026
Created by Jozef Baros
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 85 Lectures ( 8h 19m ) | Size: 5.6 GB
A practical playbook for using AI safely on real networks.

What you'll learn

⚡ Judge in seconds whether a task is a good fit for AI, ground every answer in real device data, and switch between a cloud API and a local Ollama model
⚡ Generate and validate configuration safely with a five-part prompt, few-shot examples, and a diff → dry-run → human safe-apply loop
⚡ Filter and triage syslog into ranked, structured events, and run a stateful troubleshooting co-pilot grounded in live, read-only device output via Netmiko
⚡ Automate as-built documentation, diagrams-as-code, change runbooks, and AI-assisted network design reviews
⚡ Harden API calls for production, parse multi-vendor show output properly, build a RAG pipeline over your own docs, and build a bounded, tool-calling agent

Requirements

❗ Comfortable working at the CLI on Cisco IOS/NX-OS and/or Juniper Junos — CCNP/CCIE-level or equivalent hands-on operational experience
❗ Able to read and write basic Python (variables, functions, loops) — no machine learning background needed, and none is taught
❗ A lab or workstation where you can install Python packages; a Cisco/Juniper lab is helpful for the live-grounding lessons but not required for most of the course
❗ Either an API key for a cloud model (e.g. Anthropic or OpenAI) or a machine that can run a small local model via Ollama — setup for both is covered step by step in Section 1
❗ No prior AI or LLM experience required

Description

Before enrolling, please watch the free preview lessons — so you know exactly what you're getting.
Network engineers have always automated themselves out of repetitive work — from typing commands, to Expect scripts, to Jinja2 templates, to model-driven configuration. Large Language Models are the next layer in that same line, and this course teaches you to use them as a serious working tool: not a chatbot novelty, but a co-worker that reads logs faster than you can, drafts configuration in your house style, and turns a wall of show output into a plain-English answer — without ever letting a probabilistic tool make an unsupervised change to your network.
This is a hands-on, code-first course. Almost every lecture ends in something you can actually run, and the six sections build toward three complete, deployable projects. You will work with bothCisco IOS/NX-OS and Juniper Junos throughout, because to a language model both are simply text, and most real networks are mixed. Every script is written so you can point it at acloud API (Claude/OpenAI-compatible) or a local model running on your own hardware via Ollama by changing a single line.
What the course covers, section by section
✨Foundations — what actually changed with LLMs, tokens and context windows, why models hallucinate (and the five levers that stop it), setting up a reusable Python toolkit, and writing prompts that reliably return clean, structured JSON.
✨Core Use Cases — generating and validating configuration with a safe apply loop (diff, dry-run, human gate), filtering and triaging syslog at scale, running a stateful troubleshooting co-pilot grounded in live device output, automating documentation and diagrams-as-code, and getting an AI-assisted second opinion on a network design.
✨Building with Python & APIs — production-grade API calls with retries and cost tracking, feeding the model properly parsed multi-vendor data, building a retrieval-augmented ("ask your own network") pipeline over your own documents, and building a bounded, tool-calling agent with hard guardrails.
✨Tools, Models & Operations — mapping the AI-for-networking tooling landscape, running capable models entirely on your own hardware, and the security, privacy, and cost controls that make AI safe to run at organisational scale.
✨End-to-End Projects — three complete tools you build and could genuinely deploy: a production syslog triage bot, a fleet-wide configuration compliance checker with CI integration, and an interactive, read-only troubleshooting assistant.
✨LLM Agents & MCP Servers — the Model Context Protocol standard for tool integration, building a production-ready MCP server in Python that exposes your network toolkit, connecting Claude Desktop and Claude Code to your own tools over stdio, and writing a persistent chat agent that discovers tools dynamically instead of hard-coding them — cloud or local reasoning, same guardrails, one protocol.
Every lecture that has runnable code also includes apractical, hands-on exercise, and most come with a downloadablesolution file so you can check your work.
The idea that runs through the whole course: ground the model in real data instead of letting it recall, treat every output as a draft, and let a deterministic check — a diff, a dry-run, an allow-list, or a human — decide what actually touches your network. Hold that, and everything else is detail.
The course contains the use of AI.

Who this course is for

⭐ Working network engineers (CCNP/CCIE-level operators) who want to use AI as a practical daily tool, not a novelty
⭐ NetDevOps practitioners and automation engineers who already script and template configuration and want to add AI to that toolkit responsibly
⭐ NOC and operations engineers who want to triage logs and troubleshoot faster without adding risk to the network
⭐ Team leads and architects evaluating how to introduce AI into network operations safely, with real guardrails and governance
⭐ Not a fit for: complete programming beginners, or anyone looking for AI/ML theory rather than hands-on network tooling

Homepage

https://www.udemy.com/course/ai-for-network-engineers

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