⚙️ AI Engineering Week 1 of 14 BSc · Y4 ⏱ ~50 min

Week 1: Scalability, Trade-offs & Production Architecture Patterns

Design AI systems at scale: architectural trade-offs, scalability patterns, load balancing, system design case studies, and production architecture reviews.

University of Aliens

AIE401 — Lecture 1 · BSc Y4

🎬 CC Licensed Lecture

0:00 / —:—— 📺 Creative Commons Licensed

🎯 Learning Objectives

Apply CAP theorem trade-offs to AI system design decisions
Design horizontally scalable AI serving architectures
Evaluate build vs buy decisions for AI infrastructure
Conduct architecture reviews and propose improvements

Topics Covered This Lecture

CAP Theorem & Consistency Models

Scalable AI System Patterns

Architecture Decision Records (ADRs)

Case Studies: Recommendation, Vision, LLM Systems

📖 Lecture Overview

This first lecture establishes the foundational framework for AI System Architecture & Design. 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
        Design AI systems at scale: architectural trade-offs, scalability patterns, load balancing, system design case studies, and production architecture reviews. 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: CAP Theorem & Consistency Models, Scalable AI System Patterns, Architecture Decision Records (ADRs), Case Studies: Recommendation, Vision, LLM Systems. 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 AIE401
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: CAP Theorem & Consistency Models and Scalable AI System Patterns. 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

AIE401 Project 1: AI System Design Document

Write a complete system design document for a production-scale AI application (e.g., a personalized recommendation engine handling 100K QPS). Include architecture diagrams, trade-off analysis, and capacity planning.

System design document (10-15 pages)
Architecture diagrams (sequence, component, deployment)
Capacity planning with cost estimates
ADR for 3 key architectural decisions

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

Explain the CAP theorem. Give an example of how it applies to a distributed ML serving system.

Analysis Short Answer

Compare vertical and horizontal scaling for an ML inference service. When does each make sense?

Applied Code / Proof

Design the high-level architecture for a recommendation system serving 100K requests per second.