Designing Machine Learning Systems versus The Hundred-Page Machine Learning Book.
Both show up on every "best" list. They're not competitors. They're a sequence. Here's which one to read first, and when.
Reviewed by Ashish Sheth · Updated May 2026
Author
Chip Huyen
Andriy Burkov
Pages
368
160
Published
2022
2019
Publisher
O'Reilly Media
Andriy Burkov (self-published)
Level
intermediate
beginner to intermediate
Amazon Rating
4.6/5 (933)
4.6/5 (1,400)
Goodreads Rating
4.44/5 (1,102)
4.25/5 (1,466)
Designing Machine Learning Systems
Strengths
+ Covers the entire ML lifecycle from data to monitoring
+ Focuses on principles that outlast specific tools
+ Clear and accessible writing for complex topics
+ Production-focused, not just academic theory
Caveats
− High-level overview may feel shallow for experienced ML engineers
− Limited LLM coverage (published pre-ChatGPT in 2022)
− Not enough specific code examples or tool recommendations
The Hundred-Page Machine Learning Book
Strengths
+ Distills a massive field into ~150 readable pages
+ Endorsed by Peter Norvig and other ML luminaries
+ Author follows up with companion books for deeper dives
+ Costs less than a textbook chapter and is more useful
Caveats
− Genuinely a survey, not a tutorial — you cannot build models from this alone
− Some math notation moves fast for absolute beginners
− Not enough depth to prepare for ML engineer interviews at top companies
The verdict
Read The Hundred-Page Machine Learning Book first to build foundations, then move to Designing Machine Learning Systems for advanced concepts.
Designing Machine Learning Systems
Check Price on Amazon →
The Hundred-Page Machine Learning Book
Check Price on Amazon →
Frequently asked
Which is better, Designing Machine Learning Systems or The Hundred-Page Machine Learning Book?
Read The Hundred-Page Machine Learning Book first to build foundations, then move to Designing Machine Learning Systems for advanced concepts.
Is Designing Machine Learning Systems still relevant in 2026?
The core principles of data management, evaluation, and monitoring apply to any ML system, including LLMs. But for LLM-specific topics, pair it with the author's newer book, AI Engineering.
Can I really learn machine learning in 100 pages?
You can learn the vocabulary, the major algorithms, and how to think about ML problems. You cannot learn to build production models. Treat this as a map, not a tutorial.