ISBN: 9781041166436 - the ai cloud infrastructure blueprint: practical designs and configurations for scalable ai (17 resultados)

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New. Sairohith Thummarakoti is a lead architect, researcher, and multi-book author with expertise in Pega Business Process Management (BPM), AI-powered cloud infrastructure, engineering excellence, and enterprise technology. With over a decad.

Hardcover. Condición: Brand New. 2nd edition. 216 pages. 10.00x7.00x10.24 inches. In Stock.

Hardcover. Condición: new. Hardcover. This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud. This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condición: new. Hardcover. This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud. This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condición: new. Hardcover. This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud. This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Buch. Condición: Neu. The AI Cloud Infrastructure Blueprint | Practical Designs and Configurations for Scalable AI | Sairohith Thummarakoti (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2026 | Productivity Press | EAN 9781041166436 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud. …