Description

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.