https://github.com/serodriguez68/designing-ml-systems-summary

Summary of Designing Machine Learning Systems

This is a very detailed summary of Designing Machine Learning Systems by Chip Huyen. All credit to her and O'Reilly.

I took these note for my own learning and future reference. I'm ok with PRs to improve sections.

Context before you read this book

As of 2022, the vast majority of ML applications in production are **supervised ML** models. This book focuses almost exclusively on putting supervised ML models in production.

This book won't teach you how to do ML modelling. Furthermore it assumes that you have at least a high level understanding of ML modelling.

Navigating this Book

This book can be navigated in two ways:

1. From the perspective of the components of an ML system.

2. From the perspective of the never-ending iterative process required to design, operate and continually improve an ML system in production.

3. Book navigation from the perspective of the components of an ML system

2. Book navigation from the perspective of the iterative process to build ML systems

Table of Content

Chapter 1: Overview of ML Systems

• When to use ML (and when not to)
  • Problem characteristics needed for ML solutions to work
  • Problem characteristics that will make ML solutions especially useful
  • Typical ML use cases
• ML in research vs production