Title: Outcomes & Rubrics
Course: AIINFRA 100: AI Systems & Development Environments > Section: 00 · Course Information

Outcomes & Rubrics — AIINFRA 100

Course Learning Outcomes (Canvas Outcomes)

Each outcome uses a mastery scale (4 = Exceeds, 3 = Meets/Mastery, 2 = Approaching, 1 = Beginning); 3 = mastery threshold.

# | Outcome | Program SLO / labor alignment
CLO1 | Define key AI-infrastructure concepts and identify appropriate compute, data, and software components for a given AI workload | Program: AI-infrastructure literacy · Roles: all
CLO2 | Compare cloud, on-premises, and hybrid deployment models against real requirements, cost, and constraints | Program: architecture · Roles: Cloud Eng, Systems Admin
CLO3 | Configure and customize a professional AI development environment (VS Code, Python 3.11+, extensions) | Program: tooling · Roles: all developer roles
CLO4 | Use Git and GitHub for version control and collaboration, and manage reproducible environments with venv, conda, and uv | Program: SDLC & reproducibility · Roles: all engineering, MLOps
CLO5 | Use the core Python data-science/ML toolchain (NumPy, pandas, Jupyter, scikit-learn, intro PyTorch) and track experiments with MLflow | Program: ML tooling · Roles: Data Scientist, ML Eng

Reusable Rubrics