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Module 01 · Page 2 of 20 · 7 sections

Introduction to AI System Design

What AI system design is, why it matters, the end-to-end architecture and the goals every design is judged against.

02Requirements and Use Case Analysis03Data Pipeline and Data Engineering15MLOps and LLMOps
1.1

What is AI System Design?

  • AI system design is the process of designing end-to-end systems that use AI/ML models to solve real-world problems at scale.
  • It includes the data pipeline, model training, model serving, infrastructure, monitoring, security and continuous improvement.
1.2

Why is it Important?

  • Helps build scalable, reliable and production-ready AI applications.
  • Bridges the gap between model development and real-world deployment.
  • Considers non-functional requirements like latency, cost, security and reliability.
  • Essential for real-world AI products like ChatGPT, recommendation systems and fraud detection.
1.3

High Level Architecture

Data Sources
Data Ingestion & ProcessingETL / ELT
Data StorageData lake / database
Monitoring & Feedback
Model ServingAPI / online / batch
Model Training & Evaluation
Data flows through each stage, then monitoring feeds logs, user behaviour and model performance back into the start of the pipeline.
1.4

Key Components

1
Data Sources
Collect data from various sources (DB, files, APIs, user behaviour, etc.).
2
Data Pipeline
Clean, transform and prepare data for training and inference.
3
Storage
Data lake / database to store raw and processed data.
4
Model Training
Train and validate ML/LLM models.
5
Model Serving
Deploy the model for real-time or batch inference.
6
Monitoring
Track performance, latency, drift and errors.
7
Feedback Loop
Use real user feedback to improve the model.
Related03Data Pipeline and Data Engineering04Model Training Pipeline05Model Serving and Inference Architecture13Monitoring, Logging and Observability
1.5

Types of AI Systems

1
Predictive AI
Demand forecasting, churn prediction.
2
Generative AI
Text, image and video generation (LLMs, diffusion models).
3
Recommendation Systems
Product recommendations, content recommendations.
4
Agentic AI
AI agents that can plan, use tools and take actions.
5
Real-time AI
Fraud detection, real-time personalization.
Related06LLM System Design07RAG System Design08Agentic AI System Design
1.6

Design Goals

1
Scalability
Handle large data and high traffic.
2
Reliability
High availability and fault tolerance.
3
Low Latency
Fast response time.
4
Cost Efficiency
Optimize infrastructure cost.
5
Security & Privacy
Protect data and models.
6
Maintainability
Easy to update and scale.
7
Observability
Monitor, log and debug issues.
Related11Reliability, Fault Tolerance and High Availability12Security, Privacy and Responsible AI13Monitoring, Logging and Observability
1.7

Real-world Examples

SystemWhat it does
ChatGPTLLM-based conversational AI
NetflixRecommendation system
UberETA prediction using ML
GoogleSearch ranking with ML
OpenAI / AnthropicLarge language models
SpotifyPersonalized music recommendations