AI Engineering Lab: Learning Path

The program is one continuous arc, not 24 separate courses. Here is the visual map, and the skill map underneath it.

The arc

The same arc as a live diagram (renders on GitHub):

%%{init:{"theme":"base","fontFamily":"Helvetica Neue,Helvetica,Arial,sans-serif","flowchart":{"curve":"basis","padding":14,"nodeSpacing":45,"rankSpacing":55},"themeVariables":{"fontSize":"15px","background":"#FFFFFF","primaryColor":"#EEF2F7","primaryTextColor":"#14213D","primaryBorderColor":"#14213D","secondaryColor":"#FFF1E3","secondaryTextColor":"#14213D","tertiaryColor":"#E7F4F1","tertiaryTextColor":"#14213D","lineColor":"#64748B","textColor":"#14213D","edgeLabelBackground":"#FFFFFF","clusterBkg":"#F5F8FC","clusterBorder":"#94A3B8","nodeBorder":"#14213D","mainBkg":"#EEF2F7","titleColor":"#14213D"}}}%%
flowchart TB
    subgraph P1["Phase 1 · Foundations (W1 to 4)"]
        W1["W1 Python + AI Engineering Landscape"]
        W2["W2 Data Engineering & SQL"]
        W3["W3 Machine Learning"]
        W4["W4 Deep Learning (PyTorch)"]
        W1 --> W2 --> W3 --> W4
    end
    subgraph P2["Phase 2 · LLM Core (W5 to 8)"]
        W5["W5 Tokens → Transformers"]
        W6["W6 Prompt & Context Engineering"]
        W7["W7 RAG · Vectors · Graphs"]
        W8["W8 Open Models · GPUs · Ollama"]
        W5 --> W6 --> W7 --> W8
    end
    subgraph P3["Phase 3 · Model Engineering (W9 to 11)"]
        W9["W9 Quantization & Serving"]
        W10["W10 Fine-Tuning (LoRA/DPO)"]
        W11["W11 Evals & Error Analysis"]
        W9 --> W10 --> W11
    end
    subgraph P4["Phase 4 · Harnesses & Loops (W12 to 13)"]
        W12["W12 Coding-Agent Harnesses"]
        W13["W13 Agentic Loops & Specs"]
        W12 --> W13
    end
    subgraph P5["Phase 5 · Agents (W14 to 17)"]
        W14["W14 Agent Fundamentals"]
        W15["W15 LangGraph & Frameworks"]
        W16["W16 Multi-Agent & MCP"]
        W17["W17 OpenClaw · Hermes · Ops"]
        W14 --> W15 --> W16 --> W17
    end
    subgraph P6["Phase 6 · Cloud AI Platforms (W18 to 20)"]
        W18["W18 Azure AI Foundry"]
        W19["W19 Google Vertex AI"]
        W20["W20 AWS Bedrock"]
        W18 --> W19 --> W20
    end
    subgraph P7["Phase 7 · Databricks Zero to Hero (W21 to 24)"]
        W21["W21 Unity Catalog & Lakehouse"]
        W22["W22 PySpark · Streaming · Lakeflow"]
        W23["W23 Model Training · Serving · Genie"]
        W24["W24 DABs · Governance · Capstone"]
        W21 --> W22 --> W23 --> W24
    end
    P1 --> P2 --> P3 --> P4 --> P5 --> P6 --> P7
    P7 --> GRAD["🎓 Graduate: portfolio + eval harness + governed lakehouse"]

The skill map underneath (Andrew Ng, The AI Engineering Skills Map, 2026)

The four skills are not four separate weeks, they layer on top of each other:

Ng skill	Primary weeks	You practice it as…
Building & deploying AI applications (blocks + evals/error analysis)	3 to 11, 14 to 16, 21 to 23	Every model and agent ships with a metric, an eval, and error analysis
Software engineering fundamentals (named tradeoffs)	1 to 2, then every week	Each use case names cost, scale, reliability, speed, security, privacy tradeoffs
Using coding agents (managed context, verifiers, loops)	12 to 13, then every week	From Week 12 you build with agents: spec, rules file, verifier, loop
Shaping the build (user, spec, refused tradeoff)	12 to 13, 24	Every use case starts with: who is the user, what is the spec, what we refuse

And the three loops run continuously:

%%{init:{"theme":"base","fontFamily":"Helvetica Neue,Helvetica,Arial,sans-serif","flowchart":{"curve":"basis","padding":14,"nodeSpacing":45,"rankSpacing":55},"themeVariables":{"fontSize":"15px","background":"#FFFFFF","primaryColor":"#EEF2F7","primaryTextColor":"#14213D","primaryBorderColor":"#14213D","secondaryColor":"#FFF1E3","secondaryTextColor":"#14213D","tertiaryColor":"#E7F4F1","tertiaryTextColor":"#14213D","lineColor":"#64748B","textColor":"#14213D","edgeLabelBackground":"#FFFFFF","clusterBkg":"#F5F8FC","clusterBorder":"#94A3B8","nodeBorder":"#14213D","mainBkg":"#EEF2F7","titleColor":"#14213D"}}}%%
flowchart LR
    A["🤖 Agentic coding loop<br/>(minutes)"] --> B["🧑‍💻 Developer feedback loop<br/>(hours)"]
    B --> C["🌍 External feedback loop<br/>(days to weeks)"]
    C -->|"updates vision & spec"| A

• Agentic coding loop: from Week 12: agent writes → tests → you verify against spec.
• Developer feedback loop: every Friday use case: you review, steer, and update the spec.
• External feedback loop: Weeks 13, 17, 24: share the artifact with someone real, capture what breaks, and feed it back into the spec and the evals.

The ZoroLogistics case study arc

%%{init:{"theme":"base","fontFamily":"Helvetica Neue,Helvetica,Arial,sans-serif","flowchart":{"curve":"basis","padding":14,"nodeSpacing":45,"rankSpacing":55},"themeVariables":{"fontSize":"15px","background":"#FFFFFF","primaryColor":"#EEF2F7","primaryTextColor":"#14213D","primaryBorderColor":"#14213D","secondaryColor":"#FFF1E3","secondaryTextColor":"#14213D","tertiaryColor":"#E7F4F1","tertiaryTextColor":"#14213D","lineColor":"#64748B","textColor":"#14213D","edgeLabelBackground":"#FFFFFF","clusterBkg":"#F5F8FC","clusterBorder":"#94A3B8","nodeBorder":"#14213D","mainBkg":"#EEF2F7","titleColor":"#14213D"}}}%%
flowchart LR
    D["Week 1<br/>Synthetic shipment data"] --> C["Week 2<br/>Clean, profile, SQL"]
    C --> M["Week 3 to 4<br/>ETA prediction (ML → DL)"]
    M --> L["Week 5 to 8<br/>LLM: docs, prompts, RAG, local model"]
    L --> Q["Week 9 to 11<br/>Quantize, fine-tune, evaluate"]
    Q --> A["Week 14 to 16<br/>Support agent + multi-agent team"]
    A --> O["Week 17<br/>OpenClaw personal assistant"]
    O --> X["Week 18 to 20<br/>Same agent on Azure / Google / AWS"]
    X --> DB["Week 21 to 24<br/>ZoroLogistics Lakehouse Intelligence on Databricks"]

Every artifact feeds the next phase, the dataset from Week 1 becomes the feature table in Week 23; the Week 11 eval harness gates the Week 18 cloud deployment.

---

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