Building LLMs for Production 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
Option A
Building LLMs for Production
Louis-François Bouchard, Louie Peters · 2024
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Author
Louis-François Bouchard, Louie Peters
Chip Huyen
Pages
463
368
Published
2024
2022
Publisher
Towards AI
O'Reilly Media
Level
intermediate
intermediate
Amazon Rating
4.8/5 (23)
4.6/5 (933)
Goodreads Rating
4.11/5 (53)
4.44/5 (1,102)
Building LLMs for Production
Strengths
+ Clear explanations with simple analogies for complex concepts
+ Practical code examples using LangChain and LlamaIndex
+ Strong chapters on fine-tuning, quantization, and distillation
+ Endorsed by Jerry Liu (CEO of LlamaIndex) as most comprehensive LLM apps textbook
Caveats
− Title says 'Production' but lacks depth on actual hosting and serving infrastructure
− Reads more as applied research than a practical engineering guide
− Better as a reference than a cover-to-cover read
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
Choose based on your specific needs: Building LLMs for Production focuses on introduction to llm architectures, while Designing Machine Learning Systems emphasizes ml system design overview and goals.
Building LLMs for Production
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Designing Machine Learning Systems
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Frequently asked
Which is better, Building LLMs for Production or Designing Machine Learning Systems?
Choose based on your specific needs: Building LLMs for Production focuses on introduction to llm architectures, while Designing Machine Learning Systems emphasizes ml system design overview and goals.
Is Building LLMs for Production good for beginners?
You need basic Python and some understanding of what LLMs are. It's not a first-ever AI book, but it starts from fundamentals and builds up.
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.