Foundations of AI Infrastructure and Operations

As artificial intelligence models grow in size and complexity, the demand for professionals who can deploy, manage, and scale the underlying infrastructure has never been higher. Understanding how to configure compute resources, storage pipelines, and model orchestration is essential for keeping AI systems running efficiently. This text-based course prepares you to manage modern AI infrastructure. You will transition from understanding basic hardware requirements to configuring orchestration systems, monitoring model performance, and optimizing data pipelines for machine learning workloads. What you will learn: Understand the core hardware and compute requirements for AI, including CPUs, GPUs, and specialized accelerators; Configure scalable storage solutions and data pipelines designed for massive training datasets; Deploy AI workloads using containerization tools like Docker and orchestration platforms like Kubernetes; Implement basic MLOps workflows to manage model deployment, versioning, and serving; Monitor infrastructure performance, resource utilization, and cost efficiency; Apply modern security best practices and data governance policies to AI infrastructure. You will start by learning fundamental terminology and hardware concepts before progressing to configuration, container orchestration, and continuous monitoring practices. This course is designed for IT professionals, system administrators, and tech enthusiasts looking to enter the field of AI operations, with no prior infrastructure experience required. Step into the world of AI operations and start building reliable systems today.