Rewrite introduction

[profvjreddi]

The introduction currently briefly alludes towards the vision of the book and then gets into what's actually in the book. Instead, I think this needs to be completely rewritten to follow more of a conventional introduction to machine learning systems.

[profvjreddi]

Just an initial braindump

1. Introduction

1.1 What is a Machine Learning System?

• Definition and distinction from individual ML models
• Real-world examples of ML systems

  1.2 Why Machine Learning Systems Matter

• Impact on industry and everyday life
• Challenges solved by ML systems

  1.3 Anatomy of a Machine Learning System

• Key components:
  • Data pipeline
  • Model training
  • Inference
  • Monitoring
• How these components interact

  1.4 From Model to System: The Big Picture

• Lifecycle of an ML system
• Introduction to MLOps

  1.5 Challenges in Building ML Systems

• Scalability
• Reliability
• Maintainability

  1.6 Case Studies

  A. A Day in the Life of a Large-Scale ML System

• Example: How a recommendation system works

  B. ML on the Edge: Embedded Systems

• Example: How a smart home device makes decisions

  1.7 The Future of ML Systems

• Emerging trends
• Potential career paths in ML systems engineering

  1.8 Chapter Summary and Book Overview

• Recap of key points
• Preview of upcoming chapters and how they relate to ML systems