Ai For Network Engineers Automation, Troubleshooting & Ops
 

Ai For Network Engineers: Automation, Troubleshooting & Ops
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
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