Title: Syllabus
Course: AIINFRA 303: Capstone Project

AIINFRA 303: Capstone Project — Syllabus

Course description

AIINFRA 303 is the culminating capstone of the AI Infrastructure and Architecture certificate sequence. Students design, build, deploy, and present a complete production-grade AI system of their own scoping — integrating compute, containers, cloud infrastructure, and a model or agent layer end to end. The course emphasizes professional practice: architecture documentation, infrastructure as code, CI/CD automation, security and observability, and a polished portfolio artifact suitable for employer review.

Contact hours

54 contact hours (3-unit equivalent), delivered over a 16-week term at approximately 3.4 hours/week. This is a non-credit course on the CDCP Certificate of Completion pathway.

Prerequisite

AIINFRA 100–302 — completion of the full certificate sequence is required prior to enrollment in this capstone course.

Course Learning Outcomes (CLOs)

1. CLO1: Scope, plan, and document a production-grade AI infrastructure project with clear requirements, success metrics, and architecture decision records.
2. CLO2: Design and diagram a complete end-to-end AI system architecture integrating compute, containers, cloud, and the model/serving layer.
3. CLO3: Implement, containerize, and deploy the system to the cloud with infrastructure-as-code and an automated CI/CD pipeline.
4. CLO4: Integrate a model capability (serving, fine-tuning/adaptation, RAG, or an agent/MCP layer) with security guardrails, evaluation, and observability.
5. CLO5: Load-test, right-size, document, and present a reproducible, cost-instrumented capstone with a professional portfolio artifact and demo.

Weekly schedule

Week | Topic
01 | Capstone Kickoff: Scoping a Production AI System
02 | Requirements, Success Metrics, and Architecture Decision Records
03 | System Architecture and Full-Stack Infrastructure Design
04 | Project Planning: Milestones, Risk, and a Reproducible Repository
05 | Building the Core: Containerized Services and Environments
06 | Deploying to the Cloud with Infrastructure as Code
07 | A CI/CD Pipeline for Your Capstone
08 | Design Review and Working Vertical Slice (Midterm)
09 | The Model Layer: Serving, Adaptation, or RAG Integration
10 | Adding Agentic and MCP or Retrieval Capabilities
11 | Security, Guardrails, and Responsible-AI Hardening
12 | Observability, Evaluation, and Cost Instrumentation
13 | Load Testing, Right-Sizing, and Performance Tuning
14 | Documentation, Runbooks, and Reproducibility
15 | Portfolio Packaging, Demo Prep, and Technical Storytelling
16 | Final Capstone Submission, Presentation & Course Review (Capstone)

Grading

Component | Weight
Labs | 40%
Discussions | 10%
Weekly Quizzes | 15%
Midterm | 15%
Final Capstone | 20%

Credit/No-Credit. 70% overall is required to pass.

Course policies

Academic integrity: All submitted work — code, documentation, and diagrams — must represent your own effort. You may use AI coding assistants and reference open-source code, but you must be able to explain and defend every design decision and every line of your capstone during review. Uncredited copying of a classmate's or another party's capstone architecture or code is a violation of the LACCD Student Conduct Code and will be referred accordingly. Late work: Weekly labs and quizzes are due as posted; a 10% per-day late penalty applies for up to 3 days, after which work receives no credit unless prior arrangements were made with the instructor. The Midterm design review and Final Capstone deadlines are fixed due to peer-review and presentation scheduling; extensions require documented emergency circumstances approved in advance. Responsible use of AI: This course assumes and encourages the use of AI tools (code assistants, LLMs, agents) as part of professional infrastructure practice. Students must disclose significant AI-assisted components in their capstone documentation and remain fully accountable for the correctness, security, and cost implications of anything AI-generated in their submitted system. Accessibility: This course complies with LACCD accessibility standards and Section 508/ADA requirements. Students needing accommodations should contact the campus Disabled Students Programs and Services (DSPS) office as early in the term as possible so accommodations can be arranged for labs, quizzes, and the capstone presentation.

Tools & materials

All tools are free or free-tier. No paid software or paid cloud tier is required to complete this course.

- Planning & architecture: draw.io/Excalidraw, Architecture Decision Records, GitHub Projects/Issues, Markdown docs
- Containers & orchestration: Docker, Docker Compose, Kubernetes (kind/minikube), Helm
- Cloud & IaC: AWS/GCP/Azure free tiers, Terraform, LocalStack
- CI/CD: GitHub Actions, act
- Model layer (per project): vLLM/Ollama, Hugging Face/PEFT, a vector DB (Qdrant/pgvector), LangGraph/MCP
- Security & governance: OWASP LLM Top 10, NeMo Guardrails/LLM Guard, Presidio, model cards
- Observability & FinOps: Prometheus + Grafana, Langfuse/Arize Phoenix, OpenCost/Kubecost
- Portfolio & demo: GitHub README, architecture diagrams, screen recording, slides

Week 01 · Capstone Kickoff: Scoping a Production AI System

Notion ID: 392c08fd-0278-811c-a107-c7559c4679d5 Notion URL: https://app.notion.com/p/392c08fd0278811ca107c7559c4679d5