The course “Data Centers and Infrastructure for Supporting Artificial Intelligence” provides a comprehensive understanding of the critical components and technologies that power AI infrastructure. The course explores the role of data centers as the backbone of AI systems and introduces students to the key concepts of managing and scaling AI workloads. Students will delve into the world of Kubernetes, a container orchestration platform widely used in data centers, and learn how it enables efficient deployment, scaling, and management of AI applications. They will also gain insights into NVIDIA hardware, including GPUs specifically designed for accelerating AI workloads, and understand how these powerful processors contribute to enhancing AI performance and training. Additionally, the course covers Edge AI hardware, addressing the unique challenges and requirements of running AI models on edge devices. Students will explore real-world case studies and gain hands-on experience with publicly available open-source machine learning toolkits build on top of virtualized infrastructures (i.e. Bright, Kubeflow), which streamlines the deployment and management of AI workflows in data centers.