AI Specialization Program

• Technical Tracks
AI Engineering & Data
•
Lessons

🔷 WEEK 1 Lesson 6

Title: Git & GitHub Workflow + Mini Engineering Setup Project

Lesson Objective

By the end of this lesson, learners will:

Understand why version control is critical in AI engineering

Understand repositories

Make commits properly

Understand branches

Follow basic collaboration workflow

Structure AI projects professionally

Set up their first proper AI engineering repository

This lesson builds professional discipline.

1. Why Version Control Matters

Imagine:

You trained a model.

It worked perfectly.

You changed something.

Accuracy dropped.

You don’t remember what you changed.

Without version control — you’re guessing.

With Git — you can:

Track changes

Revert mistakes

Compare versions

Collaborate safely

Maintain production systems

AI projects evolve constantly.

Version control is not optional.

It is engineering survival.

2. What Is Git?

Git is a version control system.

It tracks:

File changes

Code history

Who changed what

When it was changed

Think of it as:

Time travel for your code.

3. What Is GitHub?

GitHub is a platform that hosts Git repositories online.

It allows:

Backup

Collaboration

Code review

Project visibility

Portfolio building

Professional AI engineers always maintain clean public or private repositories.

4. Core Concepts

Repository (Repo)

A repository is:

A project folder tracked by Git.

It contains:

Code

Data scripts

README file

Project documentation.

Commit:

A commit is:

A snapshot of your project at a specific time.

Good engineers write meaningful commit messages.

Bad:

update

Good:

Refactored preprocessing function for missing values

Commit messages should explain WHY, not just WHAT.

Branches:

Branches allow you to:

Work on features separately

Experiment safely

Avoid breaking main code

Common workflow:

main → stable production code

feature-branch → development work

When ready: Merge feature branch into main.

5. Basic Collaboration Workflow

Professional workflow:

Clone repository

Create branch

Make changes

Commit changes

Push branch

Open pull request

Review & merge.

Even if working alone, practice this workflow.

It builds industry readiness.

6. Structuring AI Projects Properly

Do NOT dump everything in one folder.

Professional structure:

ai-project/

│

├── data/

│ ├── raw/

│ └── processed/

│

├── notebooks/

│

├── src/

│ ├── data_loader.py

│ ├── preprocess.py

│ ├── train.py

│ └── evaluate.py

│

├── models/

│

├── requirements.txt

├── README.md

└── .gitignore

Why this matters:

Clean separation

Easier debugging

Scalable system

Production readiness

This is how real AI systems are structured.s.

7. The README File — Your Engineering Identity

Every repository must have:

README.md

It should explain:

Problem statement

Dataset

How to install dependencies

How to run the project

Expected output

This is your professional signature.

Recruiters read README files.

Mini Engineering Setup Project

Your Assignment:

1️⃣ Create a new GitHub repository named:

ai-engineering-foundations (or create any name in this same format)

2️⃣ Structure it properly:

src/

data/

README.md

requirements.txt

3️⃣ Add:

A simple NumPy script from Lesson 4

A short README explaining the project

4️⃣ Make at least:

3 meaningful commits

Example commit structure:

Initial project setup

Added NumPy matrix computation

Added README documentation

5️⃣ Push to GitHub.

Week 1 – Lesson 6 Outcome

Students now:

✔ Understand version control

✔ Understand repositories

✔ Understand commits & branches

✔ Follow basic collaboration workflow

✔ Structure AI projects professionally

✔ Publish first engineering repository

Week 1 Complete

Students now have:

Engineering mindset

Linear algebra intuition

Probability intuition

Python discipline

Data structure awareness

Version control workflow

This is now a solid professional foundation for learners.

🔷 Week 2 — Data Engineering Lite & Model Lifecycle