Insightful Reviews of ML System Design Interview Book
Unlock ML Interview Success with Customer Insights

Insightful Reviews of ML System Design Interview Book

The book

Best for
  • Machine Learning Engineers
  • Data Scientists
  • Software Engineers
  • AI Research Scientists
  • Technical Product Managers

Starting from $18, you can purchase the complete report, offering advanced filters, buyer personas, and SWOT analysis, with Excel and PDF export options.

Purchased users
53 users purchased a report
in the last 24 hours.
One-time purchase without hidden or recurring costs!

Here, you can see the limited preview of this report for only 28 reviews.

Custom Date - Jul 18, 2024
Featured Content
  • This book really helped for preparing for my interview at a big tech company. Would 100% recommend.

    • Positive
    • Interview Preparation and Guidance
    Mar 20, 2024
  • Excellent book for anyone with any level of knowledge

    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).

    • Positive
    • Machine Learning System Design Interview
    Apr 22, 2023
  • A bit repetitive

    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)

    • None
    • Criticism on Content Accuracy
    Feb 15, 2023
  • Clear and concise ML system design guide with plenty of diagrams

    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!

    • Positive
    • Interview Preparation and Guidance
    Feb 09, 2023
  • Comprehensive resource for understanding ML systems

    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.

    - 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.

    - 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.

    • Positive
    • Practical Examples and Real-World Applications
    Feb 08, 2023
  • Great case studies, not just for interview prep

    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.

    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.

    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.

    • Positive
    • Book Structure and Organization
    Feb 04, 2023
  • See All Reviews
Sentiment Analysis (of 30 reviews)
46 Net Promoter
Languages (of 30 reviews)
Send to your inbox!
Send to your inbox!

Have the complete report in your inbox with Excel and PDF attachments. Need to play with the data? Enjoy our lightning-fast dashboard for further analysis.

One-time purchase without hidden or recurring costs!
"The most valuable form
of customer feedback"

Every purchase starts a new scraping process, so you can have a report containing the analysis of the most recent reviews. It's fast, it's easy, and it brings you the most valuable form of customer feedback.

Purchased users
53 users purchased a report in the last 24 hours.
One-time purchase without hidden or recurring costs!
Similar Reports
  1. Amazon Product Reviews 83
  2. Amazon Product Reviews 85
  3. Amazon Product Reviews 86
Frequently Asked Questions
  • This page displays purchase options and a preview of the customer feedback analysis report for Machine Learning System Design Interview based on online reviews collected from Amazon. The analysis helps Machine Learning Engineers, Data Scientists, Software Engineers, AI Research Scientists, Technical Product Managers to discover insights into what people love, dislike, and need.

  • This report provides the Net Promoter Score, sentiment analysis, content classification, most popular phrases, languages and a trend graph to display how the context changes over time. After purchasing the report, you can search in customer feedback and filter results based on classification labels and popular phrases. Also, the analysis is available in PDF format, and all the reviews are available in Excel.

  • Yes, this page is publicly available for everyone on the internet to see. You can copy and share this link with a friend, college or on social media.

  • Yes, you can, and this is the fun part about Kimola! Kimola turns customer feedback into market research by analyzing online reviews from various sources.

  • Yes, you can have this research report analyzed with more reviews. The page you are viewing displays a limited preview with the analysis of 28 recent reviews. Starting from $18, you can buy the complete version of this research report containing all reviews. Click here to see the purchase options.

Legal Disclaimer

We would like to clarify that this product is not created by Amazon and is not associated with or supported by Amazon in any way.

As a user of this web scraping tool, you are solely responsible for complying with all applicable laws and adhering to Amazon's terms of use, including copyright regulations, when utilizing the extracted review data.