Rewrite introduction
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profvjreddi commented
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 commented
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