Designing Machine Learning Systems versus Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
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
Option B
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron · 2022
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Author
Chip Huyen
Aurélien Géron
Pages
368
861
Published
2022
2022
Publisher
O'Reilly Media
O'Reilly Media
Level
intermediate
beginner to intermediate
Amazon Rating
4.6/5 (933)
4.7/5 (372)
Goodreads Rating
4.44/5 (1,102)
4.55/5 (600)
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
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Strengths
+ Most practical, code-first ML book on the market
+ Updated 3rd edition covers transformers and modern deep learning
+ Real datasets and exercises that build genuine intuition
+ Bridges classical ML and deep learning in one volume
Caveats
− 861 pages can feel intimidating for casual readers
− Heavy on TensorFlow when industry has shifted toward PyTorch
− Some chapters move fast for true beginners with no Python background
The verdict
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow first to build foundations, then move to Designing Machine Learning Systems for advanced concepts.
Designing Machine Learning Systems
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
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Frequently asked
Which is better, Designing Machine Learning Systems or Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow?
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 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.
Is Hands-On Machine Learning good for complete beginners?
If you can write Python and remember high-school math, yes. The book teaches ML concepts as you build them. You don't need calculus or linear algebra fluency to start.