AI Infrastructure and Operations Fundamentals

In this module, you will explore AI applications across various industries and delve into fundamental concepts of AI, Machine Learning (ML), and Deep Learning (DL). Additionally, the course will introduce you to Generative AI, how Large Language Models (LLMs) work and new business opportunities being unlocked with this new technology. You will understand what a GPU is, distinguish the key differences between GPUs and CPUs, and delve into the software ecosystem enabling developers to harness GPU computing for data science.

In this module, we will visit infrastructure level considerations when deploying AI clusters. You will learn about requirements for multi-system AI clusters, such as the capabilities of NVIDIA GPUs and CPUs to address the requirements of AI workloads, storage, and networking considerations. We will discuss how energy efficient computing practices help data centers lower their carbon footprint, and how recommended design documents, or Reference Architectures (RAs), can be used as a foundation for building best-of-breed optimized AI systems.

This last module covers key aspects involved in infrastructure management, monitoring, cluster orchestration, and job scheduling. You will identify the general concepts about provisioning, managing, and monitoring AI infrastructure, and describe the value and tools for cluster management. Finally, you will learn the key differences and common tools used for orchestration and scheduling, and the value of MLOps tools for continuous delivery and automation of AI workloads.

It is highly recommended that you complete all the course activities before you begin the quiz.