Title: Syllabus
Course: AIINFRA 101: Applied Infrastructure & Containerization

AIINFRA 101: Applied Infrastructure & Containerization — Syllabus

Course description

This hands-on, build-and-operate course teaches students to package, deploy, scale, and automate AI and Python workloads across the full modern infrastructure stack. Students begin by containerizing applications with Docker — building optimized, secure images and composing multi-container, GPU-enabled stacks with the NVIDIA Container Toolkit — then orchestrate those workloads on Kubernetes with deployments, services, configuration, autoscaling, and GPU-aware scheduling. From there the course moves into the cloud: provisioning cost-guarded compute, GPU instances, object storage, and IAM across the major providers, running managed and serverless Kubernetes, and managing all of it as code with Terraform. The course closes by wiring everything together with CI/CD pipelines in GitHub Actions that automatically test, build, deploy, monitor, and cost-optimize containerized model services. By the end, students can take an AI application from a Dockerfile on their laptop to an automated, observable, cost-aware deployment in the cloud.

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.

Prerequisite

AIINFRA 100, or equivalent working knowledge of Python, Git, and the command line.

Course Learning Outcomes (CLOs)

By the end of this course, students will be able to:

1. CLO1: Explain containerization and build optimized, secure Docker images for AI/Python applications.
2. CLO2: Compose and run multi-container and GPU-enabled AI stacks with the NVIDIA Container Toolkit.
3. CLO3: Deploy, scale, and manage AI workloads on Kubernetes (deployments, services, config, HPA, GPU-aware scheduling).
4. CLO4: Provision secure, cost-guarded cloud infrastructure (compute, GPU, storage, IAM) and manage it as code with Terraform.
5. CLO5: Build CI/CD pipelines with GitHub Actions that automatically test, build, deploy, and monitor containerized model services.

Weekly schedule

Week | Topic
01 | Why Containers for AI: VMs, Images, and the Docker Model
02 | Docker Fundamentals: Running & Managing Containers
03 | Building AI Images with Dockerfiles: Multi-Stage & Image Security
04 | Docker Compose & Multi-Container AI Applications
05 | GPU Containers: the NVIDIA Container Toolkit
06 | Introduction to Kubernetes: Architecture & kubectl
07 | Kubernetes Workloads: Deployments, Services, Config (Midterm)
08 | Deploying an AI Inference App to Kubernetes
09 | Scaling & GPU-Aware Scheduling: HPA and the NVIDIA GPU Operator
10 | Cloud Foundations for AI: Providers, IAM & Cost Guardrails
11 | Cloud Compute & Storage: GPU Instances, Spot, Object Storage
12 | Managed & Serverless Kubernetes in the Cloud
13 | Infrastructure as Code with Terraform
14 | CI/CD for ML with GitHub Actions
15 | Automated Model Deployment, Monitoring & Cost Optimization (Final)

Grading

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

This course is graded Credit / No-Credit. A cumulative score of 70% or higher is required to earn Credit and progress toward the Certificate of Completion.

Course policies

Academic integrity: Students are expected to submit their own work. Collaboration on concepts and troubleshooting is encouraged, but each student must independently configure, run, and submit their own labs, containers, cloud deployments, and pipelines. Submitting another student's work — code, configuration, screenshots, or quiz answers — as your own is a violation of the LACCD Student Conduct Code and may result in a zero on the assignment and referral to the college's student conduct process. Late work: Assignments are due as posted in Canvas. Late labs and quizzes are accepted for up to one week after the due date for reduced credit, at the instructor's discretion. The Midterm and Final follow stricter deadlines due to their cumulative nature; extensions require instructor approval in advance. Students facing extenuating circumstances should contact the instructor as soon as possible. Responsible use of AI: Students may use AI tools (e.g., Claude, ChatGPT, Copilot) to support learning — explaining errors, brainstorming approaches, and accelerating configuration — consistent with real-world industry practice. Students must be able to explain and reproduce any code, configuration, or pipeline they submit in their own words, and must not submit AI output they do not understand. Instructors may ask students to walk through their work verbally. Accessibility: LACCD is committed to ensuring equal access. Students with disabilities who may need accommodations should contact the campus Disabled Students Programs and Services (DSPS) office as early in the term as possible. Course materials are provided in accessible formats upon request.

Tools & materials

All tools used in this course are free or available on a free tier — no purchases are required. Students should be able to install and run:

- Containers: Docker Desktop (Docker Engine + Docker Compose), the NVIDIA Container Toolkit
- Kubernetes: kubectl plus a local cluster (kind or minikube), Deployments/Services/Ingress, Horizontal Pod Autoscaler, the NVIDIA GPU Operator
- Cloud: a free-tier cloud account (AWS, Microsoft Azure, or Google Cloud), covering compute/GPU instances, object storage (S3 / GCS / Blob), IAM, and budget guardrails
- Infrastructure as Code: Terraform (with LocalStack for offline practice)
- CI/CD & version control: GitHub and GitHub Actions (with act as a local runner), container image build/push
- Registries & security: Docker Hub, GitHub Container Registry, Trivy image scanning
- Monitoring: Prometheus, Grafana, liveness/readiness probes, cloud-native metrics (e.g., CloudWatch)

00 · Outcomes & Rubrics — AIINFRA 101