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Building Generative AI Systems on Azure: Cloud Native Architecture, Model Integration, and Cost Control for Production Applications
Paperback
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ISBN13: 9798245218601
Publisher: Independently Published
Published: Jan 22 2026
Pages: 192
Weight: 0.75
Height: 0.41 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Jan 22 2026
Pages: 192
Weight: 0.75
Height: 0.41 Width: 7.00 Depth: 10.00
Language: English
Generative AI is no longer experimental-it is becoming a core capability of modern cloud platforms. Building Generative AI Systems on Azure provides a practical, production-oriented guide to designing, deploying, and operating large-scale generative AI applications within Microsoft Azure.
This book focuses on real engineering decisions: how to structure cloud-native architectures that incorporate large language models, how to integrate managed AI services into existing systems, and how to balance performance, security, and cost at scale. Rather than treating generative AI as an isolated feature, the book shows how it fits into enterprise-grade application lifecycles.
This book focuses on real engineering decisions: how to structure cloud-native architectures that incorporate large language models, how to integrate managed AI services into existing systems, and how to balance performance, security, and cost at scale. Rather than treating generative AI as an isolated feature, the book shows how it fits into enterprise-grade application lifecycles.
You will learn how to provision and manage Azure-based AI resources, connect applications to model endpoints, and design end-to-end systems that support observability, governance, and compliance. Special attention is given to responsible AI practices, data boundaries, identity management, and secure integration with internal knowledge sources.
This book is written for engineers and architects who need clear guidance on turning generative AI capabilities into reliable cloud services-not prototypes. Each chapter emphasizes architectural patterns, deployment strategies, and operational best practices that can be applied across real-world projects.
Who this book is for
This book is written for engineers and architects who need clear guidance on turning generative AI capabilities into reliable cloud services-not prototypes. Each chapter emphasizes architectural patterns, deployment strategies, and operational best practices that can be applied across real-world projects.
Who this book is for
- Cloud and backend developers building AI-enabled services
- Solution architects designing enterprise AI platforms
- Technical leaders planning generative AI adoption on Azure
- Engineers responsible for security, cost management, and scalability
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