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EDWartens UK

Free course · AI and machine learning

Private AI with Local LLMs: Ollama, Open WebUI and Offline RAG

Run AI on your own hardware so confidential drawings, specifications and plant data never leave the building: open-weight models and their licences, GPU and RAM sizing, quantisation, Ollama, Open WebUI for a team, the API and Python, offline RAG with citations, datasheet extraction, vision models, benchmarking, air-gapped deployment and an acceptable use policy.

See the modules

Free to learn. Verifiable certificate £19 when you pass the final exam (optional, VAT included).

Level
Intermediate
Modules
13
Video
18 hours
Price
Free

Outcome

What you will be able to do

Explain when a model must run locally and classify documents by where they may go; compare open-weight models and their licences; size VRAM, RAM and speed for a model, quantisation and context length; install and configure Ollama and write Modelfiles; run Open WebUI for a team with roles and groups; call models from the REST API and Python; build an offline RAG pipeline that cites and refuses; extract structured data from datasheets and nameplates with schema validation; benchmark local against hosted models on your own test set; deploy into an air-gapped network with checksums; and write the acceptable use policy that governs it.

  • Decide which documents may go to a hosted AI tool, which must stay on a local model and which go nowhere
  • Compare open-weight models and their licences: Apache 2.0, MIT and the Llama community licence
  • Size GPU memory and speed for a model from its parameters, quantisation, context length and users
  • Install and configure Ollama, and write a Modelfile your whole team can use
  • Run Open WebUI as a private team chat server with roles, groups and model presets
  • Build offline RAG over specifications that cites the clause and says NOT FOUND
  • Extract datasheet and nameplate data with a JSON schema, validation and a human check
  • Benchmark local against hosted models, deploy air-gapped with checksums and write the acceptable use policy

Software

Ollama, Open WebUI (in Docker), Python with the ollama, chromadb and pydantic packages, and optionally LM Studio and promptfoo. All are free to download.

Hardware

A laptop with 16 GB of RAM runs the small models used in the modules. A GPU with 8 GB or more of memory, or an Apple silicon Mac with 16 GB or more, makes them faster. The sizing module shows how to size a team server; you do not need to buy one.

Syllabus

13 modules

Each module has video lessons, notes and a short quiz. The final exam closes the course.

  1. 1Why run AI locally: confidentiality, export control, cost and offline sites4 lessonsLab
  2. 2Open-weight models and their licences: Llama, Qwen, Gemma, Mistral and gpt-oss4 lessons
  3. 3Hardware sizing: VRAM, RAM, CPU-only and quantisation (GGUF, Q4, Q8)4 lessons
  4. 4Installing Ollama: the CLI, environment variables and the Modelfile5 lessons
  5. 5Open WebUI: a private ChatGPT for the team2 lessons
  6. 6The Ollama REST API and Python3 lessons
  7. 7Offline RAG over drawings, specifications and manuals4 lessonsLab
  8. 8Structured output: extracting data from datasheets2 lessonsLab
  9. 9Vision models locally: reading nameplates and P&ID snippets2 lessons
  10. 10Benchmarking local against cloud on your own tasks3 lessonsLab
  11. 11Air-gapped deployment, updates and access control3 lessons
  12. 12Governance: what may go in, logging and acceptable use3 lessonsLab
  13. 13Final assessment1 lesson

How it works

How this course works

  1. Watch the video lessonsOne module at a time, at your own pace.
  2. Take the module quizA short quiz at the end of each module shows what stuck.
  3. Keep your notesWrite in My notes beside each module and ask the trainer if you are stuck.
  4. Practise in the labOptionalTry a task from the module in the EDWartens Lab, in your browser.
  5. Sit the final examPass it once you have been through the modules.
  6. Certificate, if you want oneOptionalPaid, and only after you pass. Anyone can check it online.

EDWartens Lab

Practise this in the EDWartens Lab

Practise what makes a private AI server useful: answers that cite their source, exact structured output, and personal data removed before anything is shared.

Works alongside Private AI with Local LLMs: Ollama, Open WebUI and Offline RAG. The Lab is ai for industry practice in your browser: no install, no hardware, free to use. Sign in as a learner and it opens with your account.

Open the free lab See the Lab
AI for industry: Build AI models.

Levels to try with this course

Optional. Skip it whenever you like: it never blocks your progress or your certificate.

Why practise in a lab

Technical work is judged on what you can do: wiring, commissioning, finding a fault. Doing a task yourself teaches more than watching it a second time, and a lab lets you try it safely before you meet real equipment. It does not replace working on real hardware.

  • Practise safely, so a mistake costs nothing.
  • The lab checks your work like a site test, so you know when it is right.
  • Free, in your browser, with nothing to install.

Certificate

An optional certificate when you pass

Learning is free. Pass the final exam and you can have a certificate for £19, including VAT, with a number employers can check online. It records that you completed a free online course; it is not a regulated qualification or a CPD certificate.

  • Your name, the course, its study time and your final exam score
  • A unique certificate number and a QR code to its verification page
  • Issued by Wartens Ltd; add it to your LinkedIn profile in one click
  • Pay now or after you pass: it is issued the moment you pass the final

How the certificate works

Sample certificate for Private AI with Local LLMs: Ollama, Open WebUI and Offline RAG

Credits

Who made the lessons

The video lessons belong to their creators. EDWartens wrote the study plan, notes, quizzes and the final exam.

  • Matt Williams

    the free Ollama Course lessons on installing Ollama, the CLI, environment variables, custom models, model types, quantisation, the file layout, the OpenAI-compatible API, RAG and embeddings

  • IBM Technology

    the explainers on Ollama, open-source LLMs, privacy-preserving AI at home, open against closed AI, Llama, llama.cpp, LLM benchmarks, LLM as a judge, AI governance and AI data exposure, and the local deployment recap in the final module

  • freeCodeCamp.org

    the three-hour Ollama course by Paulo Dichone, building local AI apps with the API and Python

  • Christian Lempa

    the self-hosted Ollama and Open WebUI platform build

  • NetworkChuck

    hosting the full local AI stack on a Linux GPU server with per-user restrictions

  • Tech With Tim

    the LM Studio tutorial

  • Syntax

    the episode on local AI hardware, setup and models

  • Real Python

    running models locally with Ollama and Python

  • Sam Witteveen

    building a vision app with Ollama structured outputs

  • pixegami

    the Python RAG tutorial with local LLMs over PDFs

  • Case Done by AI

    the complete guide to Open WebUI admin settings

  • Professor Patterns

    Open WebUI knowledge bases, RAG and embeddings

  • Tony Kipkemboi

    structured outputs with Ollama and Pydantic models

  • Ian Wootten

    Ollama structured outputs in five minutes

  • Leon van Zyl

    Llama 3.2 Vision with Ollama, chatting with images locally

  • Skill Leap AI

    running OpenAI's open-weight gpt-oss models locally

  • Jason Koo

    getting started with promptfoo for testing models

  • BlueSpork

    running a model fully offline from a USB drive

  • AppSecEngineer

    the guide to the OWASP Top 10 for LLM applications

Questions

About this course

Who is this private AI and local LLM course for?
It is for engineers who want to use AI on documents they cannot paste into a hosted chatbot: process, instrument, electrical and project engineers at EPC contractors, consultancies, plants, defence and public-sector projects, and oil and gas operators worldwide. IT and OT staff who will run the server for them will find it useful too.
Is the course free to learn?
Yes. Every module, the notes, the worked problems, the project and the final assessment are free, and every piece of software used is free to download. The certificate is optional.
What do I need to know first?
You should be comfortable with a command line and able to run a short Python script. If tokens, context windows and RAG are new to you, take Generative AI and LLM Foundations first; after this course, RAG and Chatbots with LangChain goes deeper into retrieval.
What hardware do I need to run local LLMs?
A laptop with 16 GB of RAM is enough for the small models used in the modules. A GPU with 8 GB or more of memory, or an Apple silicon Mac with 16 GB or more, makes them several times faster. Module 3 teaches you to size a team server from the model, context length and number of users, so you can write the purchase request yourself.
Which software does it use?
Ollama to run the models, Open WebUI in Docker as the team chat interface, and Python with the ollama, chromadb and pydantic packages for the API, offline RAG and extraction modules. LM Studio and promptfoo appear as optional tools. Versions move every few weeks, so the notes say what was current in October 2026 and tell you to check.
Is a local model safe to use on confidential and export-controlled data?
A local model keeps the data on hardware you control, which is what most confidentiality clauses require, but it does not make the answers correct. The course teaches the data classes, the locked-down server, the logging and the acceptable use policy; export-controlled data still needs your security officer's approval for any tool, and no AI output reaches live plant without review and test.
How long does the course take?
About 18 hours of video, notes and practice at your own pace, of which about 11 hours is video. The optional team server project takes about 14 hours more.
What certificate do I get?
An EDWartens certificate of completion, issued when you finish the modules and pass the 15-question final at 60 percent, with a number anyone can verify on our site. It is not a certification from Ollama, Open WebUI or any model maker, none of whom is affiliated with EDWartens.

The Professional Module is five live days of Siemens PLC, HMI and SCADA with an instructor, then a resume session and a CPD certificate.

See the Professional Module