The AI Cloud Infrastructure Blueprint: Practical Designs and Configurations for Scalable AI

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.

Sairohith Thummarakoti is a Lead Architect, researcher, and multi-book author with expertise in Business Process Management (BPM), AI-powered cloud infrastructure, engineering excellence, and healthcare technology. With over a decade of experience, he designs scalable, intelligent systems that power automation, decision support, and digital modernization across healthcare and enterprise environments.

His core work focuses on intelligent workflow design, multi-cloud optimization, and the integration of AI with cloud-native services. He has led initiatives using low-code platforms like Pega to streamline claims automation, vaccine tracking, and oncology data platforms, while also advancing real-time analytics and infrastructure resilience in cloud ecosystems.

Sairohith has authored several books on AI, cloud architecture, and digital transformation, and holds patents in AI-based data optimization and cloud systems engineering. His research contributions appear in leading journals and conferences, and his technical interests span cloud-native development, healthcare automation, and enterprise platform modernization.

He has reviewed over 300 manuscripts for top-tier IEEE and Springer venues and regularly serves as a session chair and technical committee member. As the Founding Chair of the IEEE Computer Society - Columbia Section, he has led workshops, organized speaker sessions, and built a vibrant local chapter focused on emerging technologies.

Sairohith is also a passionate educator and mentor. He has delivered invited talks at international conferences and serves as a guest lecturer and speaker for universities and professional events. He actively supports Faculty Development Programs (FDPs), training academic and industry professionals on cutting-edge topics such as AI in healthcare, intelligent automation, and cloud infrastructure.

His work bridges academic research, industry application, and professional education empowering the next generation of engineers and architects to build systems that are not just technically sound, but truly impactful.