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// AIINFRA 101 · Semester 1
Development Environments & Tools
The practical developer toolkit for building AI projects
A hands-on introduction to the developer toolkit behind modern AI work: a professional Python setup, VS Code, Git and GitHub, reproducible environments and package management (venv, conda, and the modern uv), the Jupyter and data-science stack (NumPy, pandas, scikit-learn), deep-learning tooling (PyTorch), testing and code quality, a first look at containers and cloud development environments, and a preview of experiment tracking with MLflow. Students finish able to build, version, and share AI projects and leave with a portfolio.
01 / outcomes
Outcomes
Course objectives
- Configure and customize a professional AI development environment (VS Code, extensions, Python).
- Write, debug, and structure Python code for AI applications.
- Use Git and GitHub for version control and collaborative development (branching, pull requests).
- Manage reproducible environments and packages with venv, conda, and modern tooling (uv).
- Use the core Python data-science/ML toolchain (NumPy, pandas, Jupyter, scikit-learn) and preview experiment tracking (MLflow).
Student learning outcomes
- Configure and customize a professional AI development environment
- Write, debug, and structure Python code for AI applications
- Use Git and GitHub for version control and collaboration
- Manage reproducible environments and packages (venv, conda, uv)
- Use the Python data-science/ML toolchain and preview experiment tracking
02 / schedule
16-week schedule
Wk 01
Introduction to AI Development Environments
Orients students to the AI developer toolkit and sets up their first professional development environment.
Wk 02
Python for AI — Fundamentals
Covers core Python fundamentals needed to write, run, and debug code for AI applications.
Wk 03
Development Environment Customization (VS Code)
Customizes VS Code with extensions and settings to build an efficient, professional AI coding setup.
Wk 04
Version Control Fundamentals with Git
Introduces Git fundamentals for tracking changes and versioning AI project code.
Wk 05
Advanced Git for AI Development
Builds on Git fundamentals with advanced workflows suited to AI development projects.
Wk 06
Collaborative Development with GitHub
Uses GitHub for collaborative development, including branching and pull requests.
Wk 07
Environment & Package Management (venv, conda, uv)
Teaches reproducible environment and package management using venv, conda, and the modern uv tool.
Wk 08
Jupyter Ecosystem & Midterm
Introduces the Jupyter ecosystem for interactive AI development; this week includes the course midterm.
Midterm · covers Wks 1–7Wk 09
The Python Data Science Stack (NumPy & pandas)
Covers the Python data science stack, focusing on NumPy and pandas for data handling.
Wk 10
Machine Learning Libraries (scikit-learn)
Introduces machine learning libraries in Python, centered on scikit-learn.
Wk 11
Deep Learning Development Tools (PyTorch)
Introduces deep learning development tooling using PyTorch.
Wk 12
Testing & Quality Assurance for AI
Covers testing and quality assurance practices for AI code and projects.
Wk 13
Containerization for AI Development (Docker)
Introduces containerization for AI development using Docker.
Wk 14
Cloud Development Environments
Explores cloud development environments such as Codespaces and Colab for AI work.
Wk 15
Deployment Prep & Experiment Tracking (MLflow)
Prepares projects for deployment and previews experiment tracking with MLflow.
Wk 16
Final Project & Portfolio Development
Students complete a final capstone project and build a portfolio showcasing their work.
Capstone03 / tools
Tools & frameworks
Languages
Editor/IDE
Version Control
Environment & Package Management
Notebooks & Data Science Stack
Deep Learning, Containers & Cloud