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.
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Reviews with images
Good framework, bad details
The book gives a decent overall structure for an ML interview and follows that structure for each example. Most of it is basic and could be found anywhere. Good part is how it is applied for each situation..Unfortunately many of the details from the solutions are either not clearly written or plain wrong. One example on the picture: boosting with trees and gbdt are listed as separate and competing methods whereas they are the same. There are many errors is math parts as well. Some metrics, loss functions etc are wrong.
