⚙️ AI Engineering Week 1 of 14 BSc · Y2 ⏱ ~50 min

Week 1: Docker, Kubernetes & Infrastructure as Code

Master containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, and modern cloud infrastructure-as-code practices for AI systems.

University of Aliens

AIE203 — Lecture 1 · BSc Y2

🎬 CC Licensed Lecture

0:00 / —:—— 📺 Creative Commons Licensed (CNCF)

🎯 Learning Objectives

Containerize AI applications with Docker and Docker Compose
Deploy and scale services on Kubernetes
Write infrastructure as code with Terraform or Pulumi
Build CI/CD pipelines for AI model deployment

Topics Covered This Lecture

Docker: Images, Containers & Compose

Kubernetes: Pods, Services & Deployments

Infrastructure as Code: Terraform

CI/CD for AI: GitHub Actions & ArgoCD

📖 Lecture Overview

This first lecture establishes the foundational framework for Cloud Computing & DevOps. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        Master containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, and modern cloud infrastructure-as-code practices for AI systems. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: Docker: Images, Containers & Compose, Kubernetes: Pods, Services & Deployments, Infrastructure as Code: Terraform, CI/CD for AI: GitHub Actions & ArgoCD. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for AIE203
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: Docker: Images, Containers & Compose and Kubernetes: Pods, Services & Deployments. These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

AIE203 Project 1: Containerized AI Service Deployment

Containerize an ML inference service with Docker, deploy it on a local Kubernetes cluster (minikube or kind), and build a full CI/CD pipeline that automatically retrains and redeploys on new data.

Dockerfile and docker-compose for inference service
Kubernetes manifests (Deployment, Service, HPA)
CI/CD pipeline with automated tests and deployment
Load test results demonstrating autoscaling

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

What is the difference between a Docker image and a container? Explain image layering.

Analysis Short Answer

Describe the Kubernetes Pod restart policy. When does Kubernetes restart a failed pod?

Applied Code / Proof

Write a GitHub Actions workflow that builds a Docker image, runs tests, and pushes to DockerHub.