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  • Shipping Machine Learning Systems

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Shipping Machine Learning Systems

by El-Geish (Author) Format: Paperback
4.6 out of 5 stars (3)

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This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.
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  1. ISBN-10
    100912420X
  2. ISBN-13
    978-1009124201
  3. Publication date
    February 19, 2026
  4. Language
    English
  5. Dimensions
    6 x 1.01 x 9 inches
  6. Print length
    446 pages
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From the Publisher

Praise from Chip Huyen.
Positive endorsement from Hassan Sawaf.
Review quote from Riham Selim.
Emad Elwany endorsing book.
Review from Mohamed.

Editorial Reviews

Review

‘I love when practitioners share their hard-earned wisdom. This book doesn't shy away from the messy realities of data work, from sourcing to compliance. The case studies are especially valuable, showing how their framework holds up in real-world use cases.’ Chip Huyen, author of AI Engineering and Designing Machine Learning Systems

‘This book by Mohamed El-Geish, Shabaz Patel, and Anand Sampat is an invaluable reference for engineers and managers building best-in-class ML and AI systems. It provides practical guidance on essential considerations, methods, and tools, enabling teams to confidently navigate the complexities of real-world AI development and deployment.’ Hassan Sawaf, aiXplain

‘Shipping Machine Learning Systems is the rare book that goes beyond algorithms to show what it really takes to build production ML systems. It combines clear explanations with honest discussions of trade-offs at every stage, grounded in real examples from industry leaders like Instacart and WhatsApp. An essential guide for anyone serious about shipping robust ML products.’ Riham Selim, Meta

‘There is a significant difference between developing a machine learning system in a controlled lab environment and deploying it in production to serve real users. This book bridges that critical gap with clarity and depth. It is an invaluable resource for machine learning practitioners and application developers seeking to bring cutting-edge ML systems into the real world - reliably, safely, and at scale.’ Emad Elwany, AI Technology Executive

‘Shipping machine learning systems is where theory meets the real world, and this book delivers the practical guidance every engineer needs to succeed. It covers the unglamorous but essential work of deploying, monitoring, and scaling models in production. Having built AI systems at Kolena, I found the lessons here refreshingly real and immediately useful. This is the book I would hand any team building serious ML products.’ Mohamed Elgendy, Kolena

Book Description

Industry leaders demystify real-world ML through case studies and proven recipes for practitioners at companies of all sizes.

Product details

  • Publisher ‏ : ‎ Cambridge University Press
  • Publication date ‏ : ‎ February 19, 2026
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 446 pages
  • ISBN-10 ‏ : ‎ 100912420X
  • ISBN-13 ‏ : ‎ 978-1009124201
  • Item Weight ‏ : ‎ 1.41 pounds
  • Dimensions ‏ : ‎ 6 x 1.01 x 9 inches
  • Best Sellers Rank: #1,607,172 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.6 out of 5 stars (3)

About the authors

Follow authors to get new release updates, plus improved recommendations.
Previous page
  1. Mohamed El-Geish

    Mohamed El-Geish

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    Full content visible, double tap to read brief content.

    Mohamed is passionate about AI and empowering people to communicate effectively and securely. For a complete bio, please visit Mohamed's personal website.

    See more on the author's page
  2. Shabaz Patel

    Shabaz Patel

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    Full content visible, double tap to read brief content.

    Shabaz Patel leads AI engineering at Best Buy, shipping models that serve millions of shoppers. He previously co-founded the MLOps startup Datmo and directed climate-risk modeling at One Concern. Shabaz earned an MS with a focus in AI from Stanford and a BS from IIT. He is the author of Shipping Machine Learning Systems, a practical guide to production ML.

    See more on the author's page
  3. Anand Sampat

    Anand Sampat

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    Anand Sampat is an ML leader and serial entrepreneur who previously co-founded

    Datmo (acquired by One Concern) and has led ML teams at applied AI companies

    across multiple industries. He led ML solutions for AI-based climate risk

    mitigation for the financial sector at One Concern, new ML products for AI-based

    pathology analysis at PathAI and Enterprise ML systems delivery to logistics,

    oil & gas and technology sectors across multimodal and LLMs at SambaNova

    Systems. He holds degrees in EECS from University of California, Berkeley and

    the Stanford Artificial Intelligence Lab (SAIL).

    See more on the author's page
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