AI Networking Fundamentals for High-Performance Infrastructure

As artificial intelligence workloads scale, traditional networking protocols often become the primary bottleneck in training and inference performance. To build truly efficient AI infrastructure, you must understand how specialized high-performance fabrics handle massive data transfers. This course equips you with the foundational knowledge needed to understand, plan, and manage high-performance networks specifically optimized for AI training and distributed workloads. What you'll learn: - Understand the fundamental architecture of AI clusters and why standard Ethernet falls short - Explore InfiniBand and Remote Direct Memory Access (RDMA) technologies that enable ultra-low latency - Configure RoCE (RDMA over Converged Ethernet) and manage congestion control using PFC and ECN - Learn how Data Processing Units (DPUs) offload, accelerate, and isolate infrastructure tasks - Apply modern observability patterns to monitor network throughput and detect packet loss in real time You will start with core networking terminology and foundational hardware concepts before moving on to advanced fabric designs, congestion management, and performance tuning strategies through clear, written explanations and configuration examples. This course is designed for network engineers, system administrators, and IT professionals new to AI infrastructure, with no prior specialized hardware experience required. Start reading today to master the underlying network architectures that power modern artificial intelligence.