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6.6 MB
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4B6DCF92A51B8F42484883565800CC1F50DA2019
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March 23, 2026, 9:47 a.m.
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(Last updated: March 23, 2026, 9:48 a.m.)
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| Code.zip | 2.0 MB |
| Adedeji A. GenAI on Google Cloud. Enterprise Generative AI Systems...Agents 2026.pdf | 4.6 MB |
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SOURCE: Adedeji A. GenAI on Google Cloud. Enterprise Generative AI Systems...Agents 2026
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COVER
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MEDIAINFO
Textbook in PDF format In today's AI landscape, success depends not just on using large language models but on moving them into production in a way that's scalable, compliant, and cost-effective. GenAI on Google Cloud: Enterprise Generative AI Systems and Agents is your hands-on guide to bridging that gap. Whether you're an ML engineer tackling your first multi-agent application or a product leader responsible for scaling a GenAI strategy, this book offers a practical game plan for taking your LLM applications from promising prototype to enterprise-grade solution. Written by practitioners with deep experience in MLOps, data engineering, and GenAI infrastructure, this guide takes you through real-world workflows using Google Cloud's Vertex AI—from training and deployment to monitoring and integration. With concrete examples, field-tested frameworks, and honest insights about what actually works, you'll learn how to build production systems that deliver measurable business value. Bridge the prototype-to-production chasm that stalls 75% of LLM projects using systematic frameworks developed across hundreds of deployments Navigate the unique complexities of LLM operations through practical guidance on data orchestration, evaluation frameworks, and responsible AI practices Build enterprise-grade multimodal systems that handle text, images, and video with robust agent architectures and tool integration Implement proven optimization strategies for cost management, performance tuning, and production monitoring at scale Apply battle-tested patterns from practitioners who've guided organizations through both breakthrough successes and costly failures 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. Preface The Challenge of Generative AI Application Development Data Readiness and Accessibility Building a Multimodal Agent with the Agent Development Kit (ADK) Orchestrating Intelligent Agent Teams Evaluation and Optimization Strategies Tuning and Infrastructure MLOps for Production-Ready AI and Agentic Systems The AI and Agentic Maturity Framework Conclusion: A. Further Reading for Leaders
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