AI Infrastructure: Cloud and HPC Deployment Strategies

Scaling artificial intelligence and high-performance computing (HPC) workloads requires a solid understanding of the underlying infrastructure. Choosing the right deployment type can mean the difference between an efficient system and an expensive, underperforming project. This course equips you with the fundamental knowledge to evaluate, select, and manage the optimal infrastructure environments for modern AI applications. You will learn to navigate various deployment models—from virtual machines to containers and managed orchestration—while keeping cost, performance, and scalability in balance. What you'll learn: - Understand foundational AI infrastructure concepts, compute requirements, and specialized hardware accelerators. - Compare different deployment types including virtualized, containerized, and serverless architectures. - Configure high-performance computing (HPC) environments tailored for large-scale model training and inference. - Apply modern observability and monitoring practices to track infrastructure health and resource utilization. - Implement basic MLOps deployment patterns to streamline the transition from development to production. - Optimize infrastructure costs and performance using scaling strategies and resource allocation best practices. You will start by exploring essential terminology and hardware foundations before diving into practical deployment architectures, containerization, and workload management strategies through clear, written explanations and conceptual exercises. This course is designed for beginners with no prior cloud infrastructure experience, making it ideal for aspiring systems architects, cloud engineers, and AI practitioners. Begin your journey into the core of AI systems architecture today.