Fundamentals of AI Infrastructure and Operations

Modern artificial intelligence applications require massive compute power, specialized hardware, and robust orchestration. Operating this infrastructure efficiently is one of the most critical challenges for today's enterprise technology teams. This text-based course guides you through the foundational concepts of AI infrastructure, from understanding specialized processors to scaling complex workloads. You will learn how to plan, monitor, and optimize hardware and software environments to support machine learning models and generative AI applications. What you'll learn: - Understand the foundational hardware requirements of AI, including GPUs, TPUs, and specialized accelerators. - Configure and manage orchestration platforms like Kubernetes for scaling machine learning workloads. - Implement modern MLOps practices to streamline the deployment and monitoring of AI models in production. - Optimize data pipelines, high-performance storage, and vector databases for modern retrieval-augmented generation patterns. - Apply cost-management and energy-efficiency strategies to high-performance computing environments. The course begins with core terminology and hardware fundamentals before progressing to cluster management, orchestration, and real-world operational workflows. Through written explanations and structural breakdowns, you will build a solid operational foundation. This course is designed for IT professionals, systems administrators, and technology managers who are new to AI infrastructure and want to understand the systems powering modern models. No prior hardware engineering experience is required. Start building your understanding of the systems powering the AI revolution today.