Title: AIINFRA 100: AI Systems & Development Environments

00 · Course Information
  Outcomes & Rubrics
Week 01 — Introduction to AI Infrastructure
  Lecture — Introduction to AI Infrastructure
  Lab 1 — Stand Up Your AI Dev Environment
  Discussion 1 — Spot the AI Infrastructure
  Quiz 1 — AI Infrastructure Foundations
  Week 01 Resources
Week 02 — AI Workloads: Training vs. Inference, Latency vs. Throughput
  Lecture 2 — Training vs. Inference, Latency vs. Throughput
  Lab 2 — Measuring Latency vs. Throughput
  Discussion 2 — Picking the Right Infrastructure for the Job
  Quiz 2 — AI Workloads & Infrastructure Requirements
  Week 02 Resources
Week 03 — Computational Resources: CPUs, GPUs, TPUs, and VRAM
  Lecture 3 — CPUs, GPUs, TPUs, and the Memory That Runs Them
  Lab 3 — CPU vs. GPU Speed Test in Google Colab
  Discussion 3 — Would You Rent a GPU?
  Quiz 3 — Computational Resources for AI
  Week 03 Resources
Week 04 — The AI Software Stack: Frameworks, CUDA, Drivers, Model Formats
  Lecture 4 — Frameworks, Drivers, Containers, and Model Formats
  Lab 4 — Environment, Install, and Your First Container
  Discussion 4 — Version Mismatches and "Works on My Machine"
  Quiz 4 — AI Software Infrastructure
  Week 04 Resources
Week 05 — Python for AI — Fundamentals
  Lecture 2 — Variables, Data Structures, Functions, and Running Python Like You Mean It
  Lab 5 — Build Your Student Profile Script
  Discussion 5 — Boxes vs. Labels
  Quiz 5 — Python for AI — Fundamentals
  Week 05 Resources
Week 06 — The Developer Toolbox & VS Code as an AI Workstation
  Lecture — The Developer Toolbox & VS Code as an AI Workstation
  Lab 6 — Build Your AI Dev Profile
  Discussion 6 — Settings, Suggestions, and Surprises
  Quiz 6 — The Developer Toolbox & VS Code as an AI Workstation
  Week 06 Resources
Week 07 — Version Control with Git
  Lecture 7 — Repos, Snapshots, Branching, and Keeping Data Out of History
  Lab 7 — Initialize, Ignore, Branch, and Merge a Clean ML Repo
  Discussion 7 — Why Ignore It, Why Branch It?
  Quiz 7 — Version Control with Git
  Week 07 Resources
Week 08 — Collaborative Development with GitHub — Midterm
  Lecture 8 — From Local Commits to Shared Repositories
  Lab 8 — Your First Pull Request
  Discussion 8 — What Makes Code Review Useful?
  Midterm Exam
  Week 08 Resources
Week 09 — Environment & Package Management: venv, conda, uv
  Lecture 7 — Isolating and Reproducing Your Python Setup
  Lab 9 — Three Ways to Build the Same Project
  Discussion 9 — Pick Your Package Manager
  Quiz 9 — Environment & Package Management (venv, conda, uv)
  Week 09 Resources
Week 10 — Jupyter & the Data-Science Stack: NumPy & pandas
  Lecture 10 — The Jupyter Ecosystem and the NumPy/pandas Stack
  Lab 10 — Clean and Analyze a Real Dataset with pandas and NumPy
  Discussion 10 — Loops vs. Vectorized Thinking
  Quiz 10 — Jupyter & the Data-Science Stack (NumPy & pandas)
  Week 10 Resources
Week 11 — Machine Learning Libraries: scikit-learn
  Lecture 10 — The scikit-learn Workflow: Train, Predict, Evaluate
  Lab 11 — Classifying Iris Flowers with scikit-learn
  Discussion 11 — Picking the Right Metric
  Quiz 11 — Machine Learning Libraries (scikit-learn)
  Week 11 Resources
Week 12 — Deep Learning Tooling: PyTorch
  Lecture 11 — Tensors, Autograd, and Your First Neural Network
  Lab 12 — Build and Train a Tiny PyTorch Neural Network
  Discussion 12 — Watching the Loss Go Down
  Quiz 12 — Deep Learning Development Tools (PyTorch)
  Week 12 Resources
Week 13 — Data & Storage Infrastructure for AI
  Lecture 5 — Storing, Moving, and Trusting Your Data
  Lab 13 — Clean It, Compress It, Compare It
  Discussion 13 — Lake, Warehouse, or Both?
  Quiz 13 — Data Infrastructure for AI
  Week 13 Resources
Week 14 — Cloud, On-Prem & Hybrid: Deployment Models and Cost Awareness
  Lecture 14 — Choosing a Deployment Model, and Watching the Bill
  Lab 14 — Cloud vs. Homelab TCO Showdown
  Discussion 14 — Cloud, On-Prem, or Hybrid? Make the Call
  Quiz 14 — Deployment Models and Cost Awareness
  Week 14 Resources
Week 15 — Reproducibility, Testing & Responsible AI — Final
  Lecture 15 — Reproducible, Tested, Tracked, and Responsible AI
  Lab 15 — Lock It, Package It, Track It
  Discussion 15 — Who's Accountable When AI Gets It Wrong?
  Quiz 15 — Reproducibility, Testing & Responsible AI
  Week 15 Resources