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AI Engineering Lab: 24-Week AI Engineering Program

Find the Signal. Act with Intelligence. · Developed by Zorost Intelligence AI Lab

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

Brand new? Read START-HERE.md first, and keep 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

  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.

© 2026 Zorost Intelligence LLC · https://zorost.com