Python and Project Initialization
Learn to create a professional AI project environment using uv for package management, DVC for data versioning, and MLflow for experiment tracking.
Great work on getting your system tools set up! Now you have a solid workshop, but it's still an empty one. You've got Git, Docker, and Ollama ready to go, but we need to transform an empty folder into a real, professional AI project. The big questions we're solving today: How do you keep Python dependencies from becoming a mess? How do you handle those massive datasets that make Git unhappy? And how do you track all your experiments without losing your mind?
This tutorial is where things get really exciting. We're going to install and set up the project-level tools that will make your life so much easier: uv (the fastest Python package manager you've ever used), DVC (your solution for versioning data files), and MLflow (your digital lab notebook for tracking experiments). We'll build a real project from scratch and get everything talking to each other perfectly.
By the time we're done, you'll have transformed an empty folder into a professional AI project that any engineer would be proud of. This is the exact same setup pattern you'll use for every project throughout the academy-and probably in your career too!
Tutorial Goals
- Install the Python toolkit that will make you productive: uv, DVC, and MLflow
- Initialize a project structure that can be scaled later
- Get version control working perfectly for both your code and your data
- Create your first isolated Python environment and install packages the modern way