Engineering the Data
One idea sits under everything you have built: in machine learning and generative AI alike, the quality of your data caps the quality of your results. A brilliant model on poor data is a poor system.
What Data Engineering Means
The work is quieter than modelling, and it matters more. The essentials:
- Collect the right examples — representative of what the system will really meet
- Clean and label them — consistent, correct, free of noise
- Split train / validation / test honestly — so your score means something
- Watch for leakage and bias — both quietly inflate results and mislead you
For the depth behind each of these, the ML‑101 course is where they are taught in full.
The Through-Line
These ideas are not separate topics — they are one mindset for working with systems whose output is uncertain, the theme we opened with in Module 1.
Where to Go Deeper
This module was a bridge, not the destination. For real machine learning, take the next step:
- Training models — how they actually learn from data
- The math underneath — the ideas that make it work
- More algorithms — a wider toolbox than one course can hold
All of it lives in the ML‑101 course. This module was the bridge to it.
The Whole Map
Look back at the path you walked:
- Software literacy → coding agents → shaping the build
- LLM foundations → grounding → agents
- Evals → production → machine learning
You can now spec, build, ground, evaluate, and operate an AI tool end to end.
Build It
How to implement: it is time to build a capstone end to end, assembling the “Build It” steps from every module into one working tool.
- Weekly AI Tasks tracker — build the tracker and connect it to WhatsApp or a messaging app, using evals to keep the parser honest.
- Personal brand site — generate a truthful personal-brand site grounded in your real work, with a check that every claim has a source.
You’ve completed GenAI Engineering
From software literacy to production and data, you have the full picture. The last move is yours: pick a capstone and build it.