AI Engineering versus Designing Machine Learning Systems.
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
Chip Huyen
Pages
532
368
Published
2025
2022
Publisher
O'Reilly Media
O'Reilly Media
Level
intermediate
intermediate
Amazon Rating
4.4/5 (899)
4.6/5 (933)
Goodreads Rating
4.4/5 (1,061)
4.44/5 (1,102)
AI Engineering
Strengths
+ Clear, accessible explanations of complex AI/ML concepts
+ Practical and implementation-focused rather than theoretical
+ Well-researched with extensive references to current literature
+ Excellent for software engineers transitioning into AI development
Caveats
− Inconsistent depth: some topics feel surface-level for experienced practitioners
− Limited practical code examples
− Breadth-first approach means some topics lack deep coverage
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 verdict
Designing Machine Learning Systems is the stronger pick overall, but AI Engineering may suit you better if you're a software engineers transitioning into AI.
AI Engineering
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Designing Machine Learning Systems
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Frequently asked
Which is better, AI Engineering or Designing Machine Learning Systems?
Designing Machine Learning Systems is the stronger pick overall, but AI Engineering may suit you better if you're a software engineers transitioning into AI.
Is AI Engineering good for beginners?
You need some software engineering experience. It's not a learn-to-code book. But you don't need a PhD in ML either. If you can write Python and understand APIs, you'll follow along.
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.