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  • GenAI on Google Cloud: Enterprise Generative AI Systems and Agents

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GenAI on Google Cloud: Enterprise Generative AI Systems and Agents

by Ayo Adedeji (Author), Lavi Nigam (Author), Sarita Joshi (Author), Format: Paperback
5.0 out of 5 stars (3)

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In today's AI landscape, success depends not just on prompting large language models but on orchestrating them into intelligent systems that are scalable, compliant, and cost-effective. GenAI on Google Cloud is your hands-on guide to bridging that gap. Whether you're an ML engineer or an enterprise leader, this book offers a practical game plan for taking agentic systems from prototype to production.

Written by practitioners with deep experience in AgentOps, data engineering, and GenAI infrastructure, this guide takes you through real-world workflows from data prep and deployment to orchestration and integration. With concrete examples, field-tested frameworks, and honest insights, you'll learn how to build agentic systems that deliver measurable business value.

  • Bridge the production gap that stalls 90% of vertical AI initiatives using systematic deployment frameworks
  • Navigate AgentOps complexities through practical guidance on orchestration, evaluation, and responsible AI practices
  • Build robust multimodal systems for text, images, and video using proven agent architectures
  • Optimize for scale with strategies for cost management, performance tuning, and production monitoring
Read more
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  1. ISBN-13
    979-8341623859
  2. Edition
    1st
  3. Publisher
    O'Reilly Media
  4. Publication date
    March 3, 2026
  5. Language
    English
  6. Dimensions
    7 x 2 x 9.19 inches
  7. Print length
    317 pages
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From the Publisher

GenAI on Google Cloud: Enterprise Generative AI Systems and Agents

From the Preface

Our Approach

We believe in learning by doing. Throughout this book, we provide code examples that you can run and adapt to your specific needs. We focus on practical implementations rather than theoretical abstractions, though we provide enough theory to ensure that you understand why certain approaches work better than others.

We’ve chosen to write this book with our individual voices rather than aiming for a seamless narrative. As you read, you’ll hear from each of us directly, sharing our specific expertise and experiences. We believe this approach makes the content more authentic and allows us to connect with you on a more personal level.

Who This Book Is For

This book is designed for several key audiences:

  • Machine learning engineers and AI engineers transitioning from traditional machine learning models to complex generative AI pipelines
  • Data teams moving from conventional analytics to AI-powered insights
  • Software developers with Python skills entering AI-first application development
  • Product managers and technical leaders responsible for AI strategy and implementation
  • Career transitioners leveraging existing technical foundations to move into AI engineering roles

While we assume familiarity with Python programming and basic machine learning concepts, we’ve structured the content to be accessible to readers with varying levels of expertise. Some familiarity with Google Cloud and Vertex AI is beneficial but not a prerequisite.

Prerequisites

To get the most out of this book, you should have:

  • Experience with Python programming
  • Basic understanding of machine learning concepts
  • Familiarity with cloud computing principles (though not necessarily Google Cloud specifically)

If you’re new to some of these areas, don’t worry—we provide references and explanations where needed, and the hands-on approach means you’ll learn as you go.

Editorial Reviews

About the Author

Ayo Adedeji is a Senior Developer Relations Engineer on Google Cloud's AI Platform team and specializes in bridging advanced AI technologies with practical developer solutions. With a background as an ML Engineer in healthcare, Ayo’s expertise spans computational biology, big data processing, and foundation models. He holds engineering degrees from Stanford and Johns Hopkins and is passionate about helping developers across industries harness the power of Google Cloud to build innovative, responsible AI solutions.

Lavi Nigam is a Machine Learning Engineer and AI/ML Advocate at Google, passionate about democratizing AI and making it accessible to all. He currently leads the charge in bringing Gemini, Google's cutting-edge generative AI model, to developers worldwide through the Google Cloud Vertex AI ecosystem. In addition, he is focused on building scalable LLMOps and Generative AI Agents design patterns to help enterprises efficiently use, manage, and deploy these powerful models. His deep understanding of MLOps and Google Cloud's infrastructure empowers him to guide businesses in building robust, scalable, and production-ready AI systems. He is a recognized thought leader in the field, named one of the "40 Under 40 Data Scientists" by Analytics India Magazine.

Sarita Joshi, an AI/ML Engineer at Google Cloud, Senior IEEE member, empowers healthcare organizations to achieve transformative outcomes with AI. Her expertise is built on years of leading AI initiatives at Google and Amazon Web Services, where she served as Senior Science Manager and spearheaded customer transformations. With a background spanning consulting, R&D, and product engineering at industry giants like Amazon, Accenture, and Philips Healthcare, Sarita brings a unique blend of technical acumen and strategic vision. Her contributions extend to the research community through speaking engagements and peer review work at leading AI conferences such as ACM, NeurIPS, AAAI, and IEEE. Sarita holds a Master's degree in Computer Science from Northeastern University, equipping her with the knowledge and experience to guide others in navigating the complexities of AI in healthcare.

Stephanie Gervasi is a Senior Customer Engineer in AI/ML with Google Cloud. Steph has worked in academia, industry, and in the non-profit sector to imagine, build, and deploy AI/ML solutions. She has managed and led strategy development for Data Science and Predictive Analytics teams and created the first Responsible AI Playbook and Technical Toolkit for Fair AI at a national health payer organization. Steph has given local and international talks on AI/ML and has over 25 peer-reviewed publications, including collaborative research papers with academic institutions such as MIT and the University of Pennsylvania. Steph received a PhD in Infectious Disease Dynamics from Oregon State University and a Master’s in Ecological Sciences from the University of Michigan.

Product details

  • ASIN ‏ : ‎ B0FQB581SF
  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ March 3, 2026
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 317 pages
  • ISBN-13 ‏ : ‎ 979-8341623859
  • Item Weight ‏ : ‎ 1.23 pounds
  • Dimensions ‏ : ‎ 7 x 2 x 9.19 inches
  • Best Sellers Rank: #533,232 in Books (See Top 100 in Books)
  • Customer Reviews:
    5.0 out of 5 stars (3)

About the authors

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  1. Sarita Joshi

    Sarita Joshi

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    Sarita A. Joshi, an AI Specialist at Google Cloud and a BCS Fellow, empowers healthcare organizations to achieve transformative outcomes with AI. Her expertise is built on years of leading AI initiatives at Google and Amazon Web Services, where she served as Senior Science Manager and spearheaded AI Centers of Excellence (CoEs). With a background spanning consulting, R&D, and product engineering at industry giants like Amazon, Accenture, and Philips Healthcare, Sarita brings a unique blend of technical acumen and strategic vision. Her contributions extend to the research community through speaking engagements and peer review work at leading AI conferences such as ACM, NeurIPS, AAAI, and IEEE. Sarita holds a Master's degree in Computer Science from Northeastern University, equipping her with the knowledge and experience to guide others in navigating the complexities of AI in healthcare.

    See more on the author's page
  2. Stephanie Gervasi

    Stephanie Gervasi

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