Local AI with Ollama & Open WebUI: Run Your Own Private AI

[rlscorner]

Local AI with Ollama & Open WebUI: Run Your Own Private AI
Published 10/2026
Created by Devops Learning, Devops Lernen
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 64 Lectures ( 4h 57m ) | Size: 1.9 GB

Masterclass Hands-On Course: Ollama, Open WebUI, AI Agents and n8n - Private AI Without the Cloud for Admins and DevOps

What you'll learn
⚡ Understanding local AI: model, runtime and interface - and why your data stays in-house
⚡ Choosing the right hardware and model, even entirely without a GPU
⚡ Installing Ollama on Linux and Windows and running it as a service on the network
⚡ Measuring and tuning speed: threads, quantization, context and mixture of experts
⚡ Building your own model variants with Modelfiles, such as a Linux admin assistant
⚡ Using the model in scripts via the command line, the REST API and the OpenAI API
⚡ Setting up Open WebUI for the team: users, knowledge base, web search, MCP and HTTPS
⚡ AI in admin work: Bash, PowerShell, logs, systemd and SSH hardening reviewed by AI
⚡ Using AI agents locally: Cline, Kilo Code, Claude Code and OpenCode with Ollama
⚡ Having a local agent write Terraform, Ansible and Docker Compose
⚡ Automating with n8n and Python: alerts, documents and whole folders
⚡ Running it safely: backups, updates, prompt injection, hallucinations and costs

Requirements
❗ NO experience with AI models required.
❗ Basic knowledge of Linux and the command line is helpful.
❗ A computer with at least 32 GB of memory (or a VM on Proxmox VE) to reproduce the content yourself - no GPU needed.

Description*This course contains the use of artificial intelligence.

Welcome to "Local AI with Ollama & Open WebUI: Run Your Own Private AI"!
Are you asysadmin, DevOps engineer, or IT professional who wants to use AI in your daily workwithout your logs, configs or customer data ending up in the cloud - with no subscription, no cost per request and full control over models and versions? Then this course is exactly right for you!

We don't stop at theory. You set up yourown AI machine, as a VM onProxmox VE or on your own computer, installOllama on Linux and Windows and run an open language modelentirely without a GPU. You measure its speed, find the right number of threads and build your own model variants with aModelfile, such as a Linux admin assistant.

Then you talk to your model from thecommand line, through theREST API and theOpenAI-compatible API - existing ChatGPT code runs locally with one changed line. WithOpen WebUI, your team gets its own chat interface with users and roles, aknowledge base (RAG) from your own documents,web search with SearXNG,tools via MCP andHTTPS behind nginx.

In the main part, you put the local AI to work ineveryday admin tasks: writing and checking Bash and PowerShell scripts, analyzing logs and the Windows event log, fixing systemd services and hardening SSH. On top of that comeAI agents:Cline and Kilo Code in the editor,Claude Code and OpenCode in the terminal, a local agent that writesTerraform and Ansible - and an honest comparison oflocal versus cloud.

Finally, you automate withn8n andPython - from an alert with AI analysis on your phone to triaging whole folders - and take care of what counts in operations:backups and updates, security and prompt injection, hallucinations and costs.

We demonstrate all of this content withinlive demos - and not through boring presentations. For every lesson, you get thecommands to follow along and thescripts and files shown as a download.

Our agenda covers topics such as
✨ Understanding local AI: model, runtime and interface - and why your data stays in-house

✨ Choosing the right hardware and model, even entirely without a GPU

✨ Installing Ollama on Linux and Windows and running it as a service on the network

✨ Measuring and tuning speed: threads, quantization, context and mixture of experts

✨ Building your own model variants with Modelfiles, such as a Linux admin assistant

✨ Using the model in scripts via the command line, the REST API and the OpenAI API

✨ Setting up Open WebUI for the team: users, knowledge base, web search, MCP and HTTPS

✨ AI in admin work: Bash, PowerShell, logs, systemd and SSH hardening reviewed by AI

✨ Using AI agents locally: Cline, Kilo Code, Claude Code and OpenCode with Ollama

✨ Having a local agent write Terraform, Ansible and Docker Compose

✨ Automating with n8n and Python: alerts, documents and whole folders

✨ Running it safely: backups, updates, prompt injection, hallucinations and costs

With this course, you gain directly applicable knowledge torun your own AI on your own hardware - private, without a subscription and for your whole team. And all of that in just a few hours.

So, take this opportunity and enroll now to make the most oflocal AI!

Who this course is for
⭐ System administrators and DevOps engineers who want to use AI in their daily work without handing data to the cloud.
⭐ IT professionals and homelab enthusiasts who want to run their own AI with Ollama, Open WebUI, agents and n8n.

Code:

https://nitroflare.com/view/FEAFFD94248D678/Local_AI_with_Ollama_%26amp%3B_Open_WebUI_Run_Your_Own_Private_AI.part1.rar
https://nitroflare.com/view/FB6D984FE70DC24/Local_AI_with_Ollama_%26amp%3B_Open_WebUI_Run_Your_Own_Private_AI.part2.rar

https://rapidgator.net/file/1e2c3a2f47424b10d8254917c95b9562/Local_AI_with_Ollama_&_Open_WebUI_Run_Your_Own_Private_AI.part1.rar.html
https://rapidgator.net/file/48345029329929ff2ea656fc222750d6/Local_AI_with_Ollama_&_Open_WebUI_Run_Your_Own_Private_AI.part2.rar.html