Cloud computing has become the foundation of modern artificial intelligence, yet many learners are introduced to cloud platforms before they understand the principles that underpin them. This book takes a different approach. Developed from a university module on Cloud Computing for Artificial Intelligence, it provides a provider-independent introduction to the architectures, technologies, and engineering practices that power contemporary AI systems. Readers will learn how to design and deploy AI applications using containers, microservices, RESTful services, databases, MLOps pipelines, orchestration frameworks, Kubernetes, large language models, AI agents, and distributed computing technologies. The book goes beyond deployment to address critical professional concerns such as security, monitoring, testing, sustainability, resource management, and cloud cost estimation. Unlike many cloud textbooks, the practical exercises are designed to run primarily on modest hardware, enabling readers to develop valuable cloud and AI engineering skills without spending substantial amounts on commercial cloud services. By focusing on transferable concepts rather than provider-specific products, the book equips readers with the knowledge required to confidently work across cloud platforms and to build scalable, reliable, secure, sustainable, and cost-effective AI solutions.