AI-Engineering-Lab README

The project's README from github.com/zorost/AI-Engineering-Lab. As one markdown file: README.md.

AI Engineering Lab, a free 24 week AI engineering training program developed by Zorost Intelligence AI Lab

License: MIT Program Notebooks Site

Start here · Browse all 24 weeks · Curriculum · Reference · Glossary · Roadmap

Developed by Zorost Intelligence AI Lab · Washington, DC · zorost.com/ai-engineering-lab

Start here

git clone https://github.com/zorost/AI-Engineering-Lab.git
cd AI-Engineering-Lab
python -m pip install -r requirements.txt
  1. New to programming or to AI? Read START-HERE.md first. It names the tools, the order, and what to do when something breaks.
  2. Open curriculum/week-01. It sets up your machine and generates the dataset every later week reuses. No GPU needed for the first eight weeks.
  3. Open the tracker in curriculum/tracking, then follow the Monday row.

Everything else in this repository is linked from the week that needs it. You never have to guess what to read next.

What this is

AI Engineering Lab is a free AI engineering course: an open, self-paced training program that takes a motivated beginner from Python to production-grade AI systems in 24 weeks. Machine learning and deep learning, large language model internals, prompt and context engineering, retrieval augmented generation with vector search, quantization, fine-tuning with LoRA and DPO, evaluation harnesses, coding-agent harnesses, AI agents and the Model Context Protocol, Azure AI Foundry, Google Vertex AI, AWS Bedrock, and a governed Databricks lakehouse from zero to hero.

It is developed by Zorost Intelligence AI Lab. It is not a two-hour prompt course. It is the curriculum the Lab would hand a new engineer joining a generative AI, applied machine learning, or Databricks modernization team: opinionated, hands-on, use-case driven, and honest about what breaks in production. If you are looking for an AI engineer roadmap you can actually run rather than read, this is that, and every week ends in something that executes.

Every week pairs one concept with one artifact:

Who it is for: software developers adding AI, analysts moving toward engineering, students and career changers, technical founders. You need a computer you can install software on, about ten hours a week, and basic computer literacy. You do not need prior Python, a GPU, or a paid API key.

Who it is not for: a research career in model architecture, or a weekend prompt workshop.

Browse the program online

The whole curriculum is published as a page you can read before cloning anything, at zorost.github.io/AI-Engineering-Lab. All 24 weeks are listed there with their objectives, filterable by phase, and each one links straight back to its folder here.

The AI Engineering Lab program site: a hero band and the filterable list of all 24 weeks

The 24 week journey

Seven phases from Foundations to the Databricks capstone, with what each phase puts in your hands

Phase Weeks What you become
1 · Foundations 1 to 4 Python, data, machine learning, deep learning, with an evaluation mindset from day one
2 · LLM core 5 to 8 Tokens, transformers, prompt and context engineering, retrieval, graphs, local models
3 · Model engineering 9 to 11 Quantization, fine-tuning with LoRA and DPO, serving, evals and error analysis
4 · Harnesses and loops 12 to 13 Claude Code, Cursor, OpenCode, DeepSeek Harness, spec-driven loops
5 · Agents 14 to 17 Single agents, multi-agent systems, MCP, OpenClaw, Hermes, agent operations
6 · Cloud AI platforms 18 to 20 Azure AI Foundry, Google Vertex AI, AWS Bedrock: one agent, three clouds
7 · Databricks zero to hero 21 to 24 Lakehouse, Unity Catalog, PySpark, Lakeflow, AI Search, Genie, production

Week by week: curriculum/README.md · Visual deep dive: curriculum/learning-path.md

How a week works

One week, four beats: study, build, ship, reflect, about ten hours in total

Beat When What you do
Study Mon to Tue The week README and the one knowledge base file it points to
Build Wed to Thu Run the notebooks, then change them and break one thing on purpose
Ship Friday The use case exercise: one artifact, one number, one honest note
Reflect Fri to Sun Ten question quiz, pass at eight, then tick the tracker row

From Week 3 on, every AI artifact ships with a metric and a short error-analysis note. That habit is the point of the program.

One company, the whole way through

The ZoroLogistics case study revisited six times across the 24 weeks

You are the AI engineering team at ZoroLogistics, a fictional freight operator. Week 1's seeded dataset becomes Week 2's SQL practice, Week 3's training data, Week 7's retrieval corpus, Week 10's fine-tuning set, Week 16's agent tools, and Week 23's feature tables. You graduate with a portfolio of interconnected artifacts, not 24 disconnected demos.

Freight is the classroom because it is regulated, traceable, and full of messy operational text. The skills transfer to aviation, manufacturing, pharma, government, and finance: the industries Zorost already serves.

What is in the repository

The repository at top level: START-HERE, curriculum, reference, zoro, data, scripts, docs, and .github

AI-Engineering-Lab/
├── START-HERE.md          # day one: install, order, what to do when it breaks
├── curriculum/            # the program: 24 week folders, manifest, Excel tracker
├── reference/             # what the weeks link to
│   ├── knowledge-base/    #   14 concept files, the reading behind each week
│   ├── skills/            #   tool guides: Claude Code, Cursor, OpenCode, Ollama
│   ├── agents/            #   agent patterns: OpenClaw, Hermes, MCP
│   ├── platforms/         #   Azure, Vertex, Bedrock, Databricks
│   ├── resources/         #   free outside courses, mapped to the week they help
│   └── GLOSSARY.md        #   every term, in plain language
├── zoro/                  # the seeded data toolkit the case study runs on
├── data/                  # generated tables land here (gitignored)
├── scripts/               # repository maintenance and checks
├── docs/                  # the program site published with GitHub Pages
└── assets/                # diagrams

The framework behind the program

The curriculum implements Andrew Ng's AI Engineering Skills Map and his three loops for building software in the AI era. Zorost adds the part the map leaves out: skills do not ship, systems do.

The four skills and the three loops, with the weeks that build each one

The Skills Map is Andrew Ng's synthesis, published in The Batch in 2026. AI Engineering Lab is an independent implementation and is not affiliated with or endorsed by Andrew Ng or DeepLearning.AI. The Lab's own reading of the map: zorost.com/ai-engineering-skills-map-training-guide.

The stack you will master

Every layer of the AI engineering stack and the weeks that install it

What it costs

Nothing. The program is MIT licensed and there is no signup. Weeks 1 to 13 have a free path using local models, free-tier APIs, or no API at all. Weeks 18 to 24 use cloud free tiers and tell you how to stay inside them. A GPU is optional until Week 8.

Getting help

  1. Re-read the full error. The last line names the problem.
  2. Check that week's exercises.md hints and the glossary.
  3. Open a GitHub issue with the week number, what you ran, and the traceback.
  4. Write [email protected] for reports, security, or conduct.

See contributing, code of conduct, and security.

About Zorost Intelligence AI Lab

Zorost Intelligence designs, ships, and operates AI and data platforms for organizations where accuracy, traceability, and compliance are non-negotiable. This program is developed by the AI Lab and published as Training 01 on zorost.com/ai-lab. The Lab's open test bench is AI Fieldwork.

Original work by Zorost Intelligence. Vendor platforms are cited in each file's Sources section, and no third-party course material is reproduced here.

License

MIT. See LICENSE. Learn freely, build freely, attribute Zorost Intelligence.

© 2026 Zorost Intelligence LLC · zorost.com · @ZorostAI · [email protected]

AI-Engineering-Lab (zorost/AI-Engineering-Lab)

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318 ·GitHub
AI-Engineering-Lab
What is this?A free, self-paced 24-week AI engineering course by Zorost Intelligence AI Lab that takes a motivated beginner from Python to production-grade AI systems: ML, LLMs, RAG, fine-tuning, agents and MCP, the major clouds, and Databricks.
Is it a real course with exercises, or just a curated list of links?A full hands-on curriculum: 43 runnable notebooks in Python, SQL and PySpark, one continuous case study across all 24 weeks, a tracker that tells you what to do next, and weekly ten-question quizzes you pass at eight out of ten.
Do I need to know Python already, or own a GPU?No prior Python is required — Week 1 sets up your machine — and no GPU either, since one is only optional from Week 8. Weeks 1-13 run without any paid API key.
Will I need to pay for cloud or API usage along the way?No signup is needed at all. Weeks 1-13 run entirely on a free path, and Weeks 18-24 use cloud free tiers and tell you how to stay inside them, so no API budget is required.
Who is this course actually for?Software developers adding AI, analysts moving toward engineering, students, career changers and technical founders — no prior coding or AI background is assumed. It is explicitly not for model-architecture research or a weekend prompt workshop.
Can I reuse or adapt the course material?Yes — it is MIT licensed, so you can learn, build and reuse freely, with a request to attribute Zorost Intelligence.
What will I actually build over the 24 weeks?One graded artifact per week on the fictional ZoroLogistics freight case: the Week 1 dataset becomes SQL practice, training data, a retrieval corpus, a fine-tuning set, agent tools and feature tables. You graduate with a portfolio of interconnected artifacts instead of 24 disconnected demos.
What does the second half of the course cover?Weeks 12-13 cover coding-agent harnesses like Claude Code and Cursor, Weeks 14-17 agents and MCP, Weeks 18-20 build one agent across Azure AI Foundry, Google Vertex AI and AWS Bedrock, and Weeks 21-24 are a Databricks lakehouse track with PySpark and Unity Catalog.
Why is the case study about a freight company?Freight is regulated, traceable and full of messy operational text, which mirrors real production conditions. The Lab says the skills transfer to aviation, manufacturing, pharma, government and finance.
What is the Andrew Ng connection?The curriculum implements Andrew Ng's AI Engineering Skills Map and his three loops for building software in the AI era, but as an independent project — it is not affiliated with or endorsed by Andrew Ng or DeepLearning.AI.
What are similar projects?ai-engineer-notebooks, nlp_course, ai-engineering-hub, awesome-ai-engineering, generative-ai-for-beginners, llm-course, llm-cookbook, ai-system-design
Which topics is it in?databricks, ai-engineering, roadmap, mlops, azure, vector-search, aws, curriculum, course, jupyter notebook, quantization, fine-tuning
Creator
zorost
Size
5.6 MB
Stars
318
Updated
CommitAug 17
Age
2mo
Commits · 90 Days