Insightful Reviews of ML System Design Interview Book

This book really helped for preparing for my interview at a big tech company. Would 100% recommend.

The book is broken down into chapters that cover different topics framed as ML system design interview topics. In each chapter, you get to learn about the overall topic, as well as the details. If you're familiar with the topics, it's easier to digest, but even if you've never heard of certain topics/concepts, they're explained very well in a precise and easy to understand manner. The authors provide sources/additional reading for different concepts in each chapter.
I'm so glad I started reading this. As a full-stack data scientist, I think it's important to be able to look machine learning systems as a whole and not just individual parts of the system and I've never come across any book that frames ML problems this way. It's applicable to real-world problems (it is real-world problems) and not just a textbook, but it does provide enough information to be considered a textbook. I've never enjoyed reading a technical book this much!
Excellent book (even if you're not interviewing).

I think Alex’s other system design book is much better than this one, because this book is a bit repetitive (a lot more on the recommendation world) and the Ml system design is similar (not on the model side, but on overall Ml architecture for each chapter)

This book on ML system design is a must-read for anyone looking to improve their skills or prepare for an interview. The 7-step framework, real-world examples, and detailed solutions to interview questions are extremely helpful. The author's insider's take on what interviewers really look for and why, is a valuable addition. Highly recommended for all levels!

I recently purchased this book with the intention of gaining a deeper understanding of how ML systems are built in practice. I was pleased with what I found in this book.

The book consists of 11 chapters, starting with an introduction that outlines a framework for approaching ML system design interview questions. The following 10 chapters each delve into a real-world system that is commonly used in the industry.

Pros:
- Practical Focus: The book's main strength lies in its focus on practical examples, which helps readers to better understand the concepts and apply them in real-world situations. This approach is particularly useful for preparing for ML system design interviews, where resources on this topic can be limited.
- Clear Explanations: Each chapter is well-explained, with clear examples and case studies that effectively illustrate the concepts. The book covers a broad range of topics, from modeling algorithms to data pipelines and practical tips for scaling ML systems. The authors have done an excellent job of discussing different solutions and the trade-offs involved in building ML systems.
- Interview-oriented: The authors provide practical tips and guidance on how to approach machine learning system design interview questions and what to expect during the interview process.
- Easy to Navigate: The book is well-organized and easy to navigate, with clear headings and subheadings that make it easy to find the information you need. The writing style is clear and concise, and the authors do an excellent job of explaining complex concepts in a simple and understandable way.

Cons:
- Limited ML Fundamentals Coverage: The book does not cover ML fundamentals and is not suitable for those who want to learn the basics of ML and related concepts.
- Domain Specificity: The authors could have covered more examples from different domains, as there are several important systems that are not covered in the book, such as generative AI, language modeling, and ETA systems.
- The book does not delve deeply into complex topics, making it potentially less suitable for staff-level engineers and above.

Overall, I found this book to be a comprehensive resource for preparing for technical ML interviews and for gaining a high-level understanding of ML systems. I highly recommend it.

The book has 11 chapters. The first chapter presents the fundamentals, and the remaining covers ten use cases. The patterns I've learned have helped me think more critically. I highly recommend it.

Good:
It is a great resource for communicating decisions in a way that is well-organized and universally understood. Two features I really liked:
1) Mind maps for each design
2) Offering a dependable and repeatable framework for tackling different ML systems. Having a strong framework is crucial, allowing the practitioner to focus on the unique aspects of the system.

Bad:
My wish was that the book could cover more aspects of the ML interview, such as ML coding and ML theory.

Other resources:
It is a tough job market out there. My friends and I have been preparing for job interviews for three months. Below is the list of materials we found helpful. Good luck, everyone!

- Stanford CS229: Machine Learning
- Deep Learning book
- Designing machine learning systems book by Chip Huyen
- She also maintains a great GitHub repo
- Made with ML
- ML system design interview guide by Patrick Halina
- Industry papers. Tiktok, YouTube, and Instagram all released great papers about recommendation systems.