Designing Machine Learning Systems versus Fundamentals of Data Engineering.
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 July 2026
Author
Chip Huyen
Joe Reis, Matt Housley
Pages
368
447
Published
2022
2022
Publisher
O'Reilly Media
O'Reilly Media
Level
intermediate
intermediate
Amazon Rating
4.6/5 (933)
4.5/5 (860)
Goodreads Rating
4.44/5 (1,102)
4.15/5 (1,019)
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
Fundamentals of Data Engineering
Strengths
+ Tool-agnostic: teaches principles that survive stack churn
+ The lifecycle framework organizes a chaotic field
+ Strong on the undercurrents most tutorials skip
+ Great onboarding for engineers new to data work
Caveats
− Deliberately high-level: few hands-on code examples
− Experienced data engineers may find early chapters basic
− Broad coverage means no single topic goes deep
The verdict
Designing Machine Learning Systems is the stronger pick overall, but Fundamentals of Data Engineering may suit you better if you're a engineers moving into data engineering roles.
Designing Machine Learning Systems
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Fundamentals of Data Engineering
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Frequently asked
Which is better, Designing Machine Learning Systems or Fundamentals of Data Engineering?
Designing Machine Learning Systems is the stronger pick overall, but Fundamentals of Data Engineering may suit you better if you're a engineers moving into data engineering roles.
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.
Is Fundamentals of Data Engineering worth reading in 2026?
Yes. Because it teaches a tool-agnostic lifecycle rather than a specific stack, it has aged well while individual tools churn. It is the standard first book for engineers entering data work and a solid framework for teams standardizing their approach.