Module 1
AI Engineering Foundations
Master the essential tools and environment setup for professional AI development
Why Study This Module:
Before building AI systems, you need a solid foundation of professional development tools. These tools are used by every AI engineer in the industry and form the backbone of modern software development.
What I Learned by Completing This:
• Version control workflows with Git for managing code changes
• Containerization with Docker for consistent environments across teams
• Python environment management with uv for fast, reliable dependency handling
• Data versioning with DVC to track large datasets without bloating repositories
• Experiment tracking with MLflow to compare model performance systematically
• Professional IDE setup for productive AI development
Real-World Importance:
In production AI systems at companies, these tools are non-negotiable:
- Git ensures team collaboration without code conflicts
- Docker eliminates "works on my machine" problems in deployment
- DVC manages terabytes of training data with version control
- MLflow tracks thousands of experiments to find the best models
- Proper tooling separates amateur projects from production-ready systems
Topics
The Command-Line Toolkit
Install Git and Docker for version control and reproducible environments
Python and Project Initialization
Set up uv, DVC, and MLflow for professional Python project management
IDE and Environment Verification
Configure VS Code/Cursor and verify your complete development toolchain