We start the journey with an Introduction to AI where we cover AI basic concepts and principles.​ Then, we delve into data center and cloud infrastructure followed by AI operations.​

Introduction to AI: • AI use cases across different industries​ • The evolution of AI into machine learning, deep learning, and Generative AI, each phase unlocking new capabilities.​ • The emergence of generative AI applications, producing content like music, images, and videos.​ • How GPUs revolutionized AI?​ • The importance of a suitable software stack ensuring optimal performance and efficiency.​​ • The environments where AI workloads run - data centers or the cloud. ​ AI infrastructure: • Compute platforms, networking, and storage for AI.​ • How energy efficient computing practices helps data centers lower their carbon footprint and energy use.​ • How recommended design documents called reference architectures (RAs), can be used as a foundation for building best-of-breed designs. ​​ • Moving from an on-prem data center to cloud based solutions that offer a flexible and accessible alternative.​ ​ AI Operations:​ • Infrastructure management and monitoring​ • Cluster orchestration and job scheduling ​

Upon completion, you should be able to:  • Express how AI is transforming society. • Explain terminology and concepts related to AI. • Describe the evolution of GPU computing and its contribution to the AI revolution. • Evaluate the NVIDIA hardware and software solutions for AI. • Identify the data center infrastructure building blocks and their role in building an AI data center. • Recall AI in the data center operation tasks

• IT Professionals • System and Network Administrators • DevOps Engineers • Datacenter professionals 

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