Title: AIINFRA 101: Applied Infrastructure & Containerization

00 · Course Information
  Outcomes & Rubrics
Week 01 — Why Containers for AI: VMs, Images, and the Docker Model
  Lecture 1 — Containers, VMs, and the Docker Model
  Lab 1 — First Contact: Containers vs. Estimated VMs
  Discussion 1 — Containers, Trust, and Untrusted Models
  Quiz 1 — Why Containers for AI: VMs, Images, and the Docker Model
  Week 01 Resources
Week 02 — Docker Fundamentals: Running & Managing Containers
  Lecture 2 — Running, Inspecting, and Managing Containers
  Lab 2 — Container Lifecycle and Inspection Toolkit
  Discussion 2 — Debugging a Misbehaving Container
  Quiz 2 — Docker Fundamentals: Running and Managing Containers
  Week 02 Resources
Week 03 — Building AI Images with Dockerfiles: Multi-Stage & Image Security
  Lecture 3 — Multi-Stage Dockerfiles, Tagging, and Image Security
  Lab 3 — Shrink, Tag, Push, and Scan an AI Inference Image
  Discussion 3 — Small, Versioned, and Verified: Image Practices in Production
  Quiz 3 — Building AI Images with Dockerfiles: Multi-Stage & Image Security
  Week 03 Resources
Week 04 — Docker Compose & Multi-Container AI Applications
  Lecture 4 — From Single Containers to Orchestrated Stacks
  Lab 4 — FastAPI + Redis + Trainer Multi-Container Stack
  Discussion 4 — Ordering the AI Pipeline
  Quiz 4 — Docker Compose and Multi-Container AI Applications
  Week 04 Resources
Week 05 — GPU Containers: the NVIDIA Container Toolkit
  Lecture 5 — Giving Containers a GPU: The NVIDIA Container Toolkit
  Lab 5 — Configure and Verify GPU Access in Docker
  Discussion 5 — Why the Daemon, Not the Container?
  Quiz 5 — GPU Containers and the NVIDIA Container Toolkit
  Week 05 Resources
Week 06 — Introduction to Kubernetes: Architecture & kubectl
  Lecture 6 — Anatomy of a Kubernetes Cluster
  Lab 6 — Standing Up and Inspecting a Local Kubernetes Cluster
  Discussion 6 — Blast Radius Without Isolation
  Quiz 6 — Introduction to Kubernetes: Architecture and kubectl
  Week 06 Resources
Week 07 — Kubernetes Workloads: Deployments, Services, Config
  Lecture 7 — Deployments, Services, and Configuration in Kubernetes
  Midterm Exam
  Discussion 7 — The Case of the Misconfigured Model Path
  Week 07 Resources
Week 08 — Deploying an AI Inference App to Kubernetes
  Lecture 8 — From Container to Cluster: Serving Models on Kubernetes
  Lab 8 — Containerize and Deploy a FastAPI Inference Service to kind
  Discussion 8 — GPU-Bound or I/O-Bound? Reading the Signals
  Quiz 8 — Deploying an AI Inference App to Kubernetes
  Week 08 Resources
Week 09 — Scaling & GPU-Aware Scheduling: HPA and the NVIDIA GPU Operator
  Lecture 9 — From HPA Mechanics to GPU-Aware Scheduling
  Lab 9 — Autoscaling a CPU-Bound App with HPA
  Discussion 9 — Beyond Average CPU: What Scaling Signals Are We Missing?
  Quiz 9 — Scaling & GPU-Aware Scheduling
  Week 09 Resources
Week 10 — Cloud Foundations for AI: Providers, IAM & Cost Guardrails
  Lecture 10 — Providers, Free Tiers, and the Guardrails That Keep Them Free
  Lab 10 — Free-Tier Account, Least-Privilege IAM User, and a Zero-Cost Budget Guardrail
  Discussion 10 — Are Free Tiers and Credits Ever Really Safe?
  Quiz 10 — Cloud Foundations for AI: Providers, IAM & Cost Guardrails
  Week 10 Resources
Week 11 — Cloud Compute & Storage: GPU Instances, Spot, Object Storage
  Lecture 11 — From VM to Bucket: The True Cost of Cloud Compute and Storage
  Lab 11 — Launch, Benchmark, and Store: Cloud Compute Meets Object Storage
  Discussion 11 — Is Spot Worth the Risk?
  Quiz 11 — Cloud Compute & Storage: GPU Instances, Spot, Object Storage
  Week 11 Resources
Week 12 — Managed & Serverless Kubernetes in the Cloud
  Lecture 12 — Serverless Compute and Managed Kubernetes for ML Workloads
  Lab 12 — Serverless and Kubernetes Model Serving, Side by Side
  Discussion 12 — Kubernetes Everywhere, or Cloud-Native First?
  Quiz 12 — Managed & Serverless Kubernetes in the Cloud
  Week 12 Resources
Week 13 — Infrastructure as Code with Terraform
  Lecture 13 — Providers, Resources, and State: Thinking in Terraform
  Lab 13 — Provision Free-Tier AWS Infrastructure with Terraform (Local and Real)
  Discussion 13 — Preventing State Drift on a Team
  Quiz 13 — Infrastructure as Code with Terraform
  Week 13 Resources
Week 14 — CI/CD for ML with GitHub Actions
  Lecture 14 — CI, CD, and Building an ML Pipeline with GitHub Actions
  Lab 14 — Build an ML CI Pipeline: Quality Gates and a Docker Image Push
  Discussion 14 — Why Doesn't Everyone Test Their Pipelines?
  Quiz 14 — CI/CD for ML with GitHub Actions
  Week 14 Resources
Week 15 — Automated Model Deployment, Monitoring & Cost Optimization
  Lecture 15 — From Commit to Monitored, Cost-Aware Production
  Lab 15 — Ship, Watch, and Right-Size- An End-to-End Deployment Pipeline
  Discussion 15 — How Much Automation and Human Review Does a Pipeline Actually Need?
  Quiz 15 — Automated Model Deployment, Monitoring & Cost Optimization
  Week 15 Resources