Title: Syllabus
Course: AIINFRA 100: AI Systems & Development Environments

Syllabus — AIINFRA 100: AI Systems & Development Environments

Course description

An on-ramp to modern AI systems and the developer toolkit used to build them. The first half builds AI-infrastructure literacy — what AI workloads are (training vs. inference), the compute, data, and software layers they depend on, and how AI systems are deployed across cloud, on-premises, and hybrid environments — carrying a prototype-vs-production mindset throughout. The second half is a hands-on developer toolkit: 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), plus testing, experiment tracking with MLflow, and responsible-AI practice. Students finish able to reason about AI infrastructure and to build, version, and share reproducible AI projects — and they leave with a portfolio. Deep container/Kubernetes/cloud work is covered in AIINFRA 101; deep security in AIINFRA 302. No prior AI or programming experience required.

Prerequisites

None. Basic computer literacy and a willingness to use a command line and a code editor are recommended; the pace is brisk and the workload is hands-on.

Contact hours

54 contact hours (3-unit equivalent), delivered over a 16-week term at approximately 3.4 hours per week; non-credit, CDCP Certificate of Completion pathway.

Required materials & technology

- A laptop/desktop capable of running a modern browser, VS Code, Python 3.11+, Git, and Docker Desktop
- A free GitHub account
- Free-tier cloud account (AWS, Google Cloud, or Azure) — no paid spend required for labs
- All software used is free/open-source; there is no required textbook (Open Educational Resources + curated readings provided each week)
- Reliable internet connection

Course Learning Outcomes (CLOs)

Upon successful completion, students will be able to:

1. Define key AI-infrastructure concepts and identify appropriate compute, data, and software components for a given AI workload.
2. Compare cloud, on-premises, and hybrid deployment models against real requirements, cost, and constraints.
3. Configure and customize a professional AI development environment (VS Code, Python 3.11+, extensions).
4. Use Git and GitHub for version control and collaboration, and manage reproducible environments with venv, conda, and uv.
5. Use the core Python data-science/ML toolchain (NumPy, pandas, Jupyter, scikit-learn, intro PyTorch) and track experiments with MLflow.

Grading scheme

This is a noncredit CDCP course graded Credit / No-Credit; ≥ 70% overall = Credit. Points are tracked so the course can also map to a letter grade if a college requires it.

Assignment group | Weight
Hands-on Labs | 40%
Discussions | 10%
Weekly Knowledge Checks (quizzes) | 15%
Midterm (Week 8) | 15%
Final Project & Portfolio (Week 15) | 20%
Total | 100%

Optional letter mapping: A 90–100 · B 80–89 · C 70–79 · D 60–69 · F \<60.

Course policies

- Participation: weekly labs and discussion expected; this is a hands-on course.
- Late work: labs accepted up to 1 week late for up to 80% credit unless prior arrangement; quizzes close at week's end.
- Academic integrity: your work must be your own; cite sources and collaborators.
- Responsible AI-use policy: AI coding assistants (Copilot, Claude, ChatGPT, Cursor) are encouraged as learning tools and are themselves course subject matter. You must (a) disclose AI use on submissions, (b) be able to explain any code/answer you submit, and (c) never submit AI output you don't understand. Using AI to complete a quiz/exam that forbids it is an integrity violation.
- Accessibility: students with disabilities should contact DSPS for accommodations; all materials are provided in accessible formats.
- Communication: questions via the course Q&A discussion or instructor email; 48-hour response target on weekdays.

Weekly schedule

Wk | Topic | Key deliverable
1 | Introduction to AI Infrastructure (prototype vs. production) | Lab + discussion
2 | AI Workloads: Training vs. Inference, Latency vs. Throughput | Lab + quiz
3 | Computational Resources: CPUs, GPUs, TPUs, and VRAM | Lab + quiz
4 | The AI Software Stack: Frameworks, CUDA, Drivers, Model Formats | Lab + quiz
5 | Python for AI — Fundamentals | Lab + quiz
6 | The Developer Toolbox & VS Code as an AI Workstation | Lab + quiz
7 | Version Control with Git | Lab + quiz
8 | Midterm • Collaborative Development with GitHub | Midterm + lab
9 | Environment & Package Management: venv, conda, uv | Lab + quiz
10 | Jupyter & the Data-Science Stack: NumPy & pandas | Lab + quiz
11 | Machine Learning Libraries: scikit-learn | Lab + quiz
12 | Deep Learning Tooling: PyTorch | Lab + quiz
13 | Data & Storage Infrastructure for AI | Lab + quiz
14 | Cloud, On-Prem & Hybrid: Deployment Models and Cost Awareness | Lab + quiz
15 | Final • Reproducibility, Testing & Responsible AI | Final project & portfolio