# AI Engineering Lab: 24-Week AI Engineering Program > **Find the Signal. Act with Intelligence.** · Developed by [Zorost Intelligence AI Lab](https://zorost.com) This is the heart of AI Engineering Lab: a 24-week, week-by-week path from Python fundamentals to production lakehouse AI. Every week has a **section** (the phase), a **category** (the skill area), a **use case** from the running ZoroLogistics case study, **runnable notebooks**, and a **checklist** you tick off in the Excel tracker. ![Seven phases across 24 weeks, and what each one puts in your hands](../assets/diagrams/lab-journey.png) > **Brand new?** Read [`START-HERE.md`](../START-HERE.md) first, and keep > [`reference/GLOSSARY.md`](../reference/GLOSSARY.md) one tab away, every term in the program is > defined there in plain language. ## How the program works ![One week, four beats: study, build, ship, reflect](../assets/diagrams/lab-week.png) 1. **One case study, all 24 weeks.** You are the AI engineering team at **ZoroLogistics**, a fictional freight company. The data, the models, and the agents you build in Week 1 are reused, improved, and productionized all the way to Week 24. By graduation you have a portfolio of interconnected artifacts, not 24 disconnected demos. 2. **Weekly cadence (≈10 hours/week).** - **Mon to Tue · Study**: read the week's README and the linked knowledge-base file. - **Wed to Thu · Build**: run the notebook(s), then modify and extend them. - **Fri · Use case**: complete the use-case exercise: ship something concrete. - **Fri to Sun · Reflect & check off**: update the Excel tracker, push to your fork. 3. **Evals everywhere.** From Week 3 onward, every AI artifact ships with a score (a metric or an eval) and an error-analysis note. That is the core AI engineering habit this program installs. 4. **Local-first, cloud-later.** Weeks 1 to 13 run on your laptop (no GPU needed until Week 8, and even then a small model on CPU works). Weeks 18 to 24 use free/limited tiers of the major clouds. ## Prerequisites - Comfort with any programming language (Python is taught from the ground up) - Git basics (Week 1 refreshes them) - A laptop: 16 GB RAM recommended; Apple Silicon or NVIDIA GPU helpful from Week 8 - Optional accounts (created during their weeks): GitHub, Hugging Face, a model API key of your choice, free tiers of Azure / Google Cloud / AWS / Databricks ## The 24 weeks at a glance | Wk | Week title | Section | Category | |---|---|---|---| | 1 | Python Foundations & the AI Engineering Landscape | Foundations | Python & Environment | | 2 | Data Engineering & SQL for AI | Foundations | Data & SQL | | 3 | Machine Learning Fundamentals | Foundations | Classical ML | | 4 | Deep Learning with PyTorch | Foundations | Deep Learning | | 5 | How LLMs Work: Tokens to Transformers | LLM Core | LLM Internals | | 6 | Prompt Engineering & the Context Window | LLM Core | Prompt & Context | | 7 | RAG, Vector Search & Knowledge Graphs | LLM Core | Retrieval & Graphs | | 8 | Open Models & Local Inference: GPUs, Ollama, llama.cpp | LLM Core | Local Models & GPUs | | 9 | Quantization & Efficient Inference | Model Engineering | Quantization & Serving | | 10 | Fine-Tuning: LoRA, SFT & DPO | Model Engineering | Fine-tuning | | 11 | Evals & Error Analysis for AI Systems | Model Engineering | Evaluation | | 12 | Coding-Agent Harnesses: Claude Code, Cursor, OpenCode, DSH | Harnesses & Loops | Harnesses | | 13 | Agentic Coding Loops & Spec-Driven Development | Harnesses & Loops | Loops & Specs | | 14 | Agent Fundamentals: The Loop, Tools & Memory | Agents | Agent Core | | 15 | Agent Frameworks: LangGraph & the State-Graph Model | Agents | Frameworks | | 16 | Multi-Agent Systems & MCP | Agents | Multi-Agent & Protocols | | 17 | OpenClaw, Hermes & Agent Operations | Agents | Personal Agents & Ops | | 18 | Azure AI Foundry | Cloud AI Platforms | Microsoft | | 19 | Google Vertex AI & Gemini | Cloud AI Platforms | Google | | 20 | AWS Bedrock & SageMaker AI | Cloud AI Platforms | AWS | | 21 | Databricks Day Zero: Unity Catalog & the Lakehouse | Databricks Zero to Hero | Platform & Data | | 22 | Databricks Data Engineering: PySpark, Streaming & Lakeflow | Databricks Zero to Hero | Pipelines | | 23 | Databricks ML & GenAI: Training, Serving, Genie | Databricks Zero to Hero | ML & GenAI | | 24 | Databricks Production: DABs, Governance & the Capstone | Databricks Zero to Hero | Production & Capstone | Each week folder (`curriculum/week-NN/`) is a complete lesson: ``` week-NN/ ├── README.md # the lesson: problem → deep concepts (tables, diagrams, │ # worked examples, pitfalls, glossary) → notebook │ # walkthrough → use case → sources ├── notebooks/ # 1 to 3 runnable Jupyter notebooks (Python / SQL / PySpark) ├── exercises.md # graded exercises + hints + the week's checklist └── quiz.md # 10-question self-check with answer key (pass 8/10) ``` ## The phases ### Phase 1 · Foundations (Weeks 1 to 4) Python, data engineering, classical ML, and deep learning, taught the AI engineering way: every model ships with a metric, a split, and an error analysis. Week 1 builds the ZoroLogistics synthetic-data generator you reuse forever after. ### Phase 2 · LLM Core (Weeks 5 to 8) How LLMs actually work, tokens, embeddings, attention, KV cache, then the two engineering superpowers on top: **prompt & context-window engineering** and **retrieval (RAG + knowledge graphs)**. Week 8 takes you local: open models, Ollama/llama.cpp/MLX, and GPU setup from CUDA to Apple Metal. ### Phase 3 · Model Engineering (Weeks 9 to 11) Make models cheaper and yours: quantization formats and serving engines, fine-tuning with LoRA/SFT/DPO, and the discipline that decides it all, evals and error analysis (Ng: the single biggest predictor of how fast a team ships an agent). ### Phase 4 · Harnesses & Loops (Weeks 12 to 13) Become dangerous with coding agents: Claude Code, Cursor, OpenCode, and the DeepSeek Harness. Learn managed context, rules files, subagents, and the three loops (agentic coding → developer feedback → external feedback) with a spec, a verifier, and a blast-radius rule. ### Phase 5 · Agents (Weeks 14 to 17) From a hand-written ReAct loop to LangGraph state graphs, multi-agent orchestration, and MCP. Week 17 runs OpenClaw as your personal lab assistant and Hermes-class open models as agent brains, then adds the ops layer: tracing, evals, and cost. ### Phase 6 · Cloud AI Platforms (Weeks 18 to 20) The same ZoroLogistics support agent, deployed three ways: Azure AI Foundry (Microsoft), Vertex AI + AI Studio (Google), Bedrock + SageMaker (AWS). Compare capabilities, governance, and cost, and learn how to pick. ### Phase 7 · Databricks Zero to Hero (Weeks 21 to 24) The full Zorost Databricks modernization playbook: Unity Catalog, Delta Lake medallion, DBSQL, PySpark, streaming, Lakeflow Pipelines & Jobs, MLflow, feature engineering, Databricks Model Training, Model Serving + Unity AI Gateway, AI Search (Vector Search), AI functions, Genie, and Agent Bricks, then DABs, CI/CD, governance, and FinOps, ending in the **ZoroLogistics Lakehouse Intelligence** capstone. ## Progress tracking Download `tracking/ai-engineering-lab-24-week-tracker.xlsx` and open it in Excel, Google Sheets, or LibreOffice. It contains: - A **Dashboard** sheet: your name, start date, per-week completion bars, and a chart of your progress across all 24 weeks. - **One sheet per week**: every checklist item with a status dropdown (☐ Not started · ▶ In progress · ✅ Done · ⏭ Skipped), automatic per-week completion percentage, and a notes column. The workbook is generated from `manifest.json`, see `tracking/README.md`. ## Certification of completion Finish all 24 weeks (including the Week 24 capstone) and you will have: - 43 executed notebooks (Python, SQL, PySpark) - A portfolio: fine-tuned model, RAG agent, multi-agent system, MCP server, three cloud deployments, a governed Databricks lakehouse - An eval harness you built yourself, the artifact that separates AI engineers from demo builders Share your fork and tracker dashboard with your mentor, your team, or [Zorost Intelligence](https://zorost.com). --- © 2026 Zorost Intelligence LLC · https://zorost.com