This source outlines the essential hardware and software components required to build an AI-ready infrastructure capable of handling large-scale workloads. It categorizes the lifecycle of artificial intelligence into training, fine-tuning, and inferencing, noting that each phase demands unique levels of computational power and data throughput. To achieve peak performance, organizations must utilize specialized accelerators like GPUs and NPUs while implementing a tiered storage strategy to manage data pipelines efficiently. High-speed network fabrics are also critical to prevent bottlenecks that leave expensive processing chips idle. Finally, the text emphasizes that integrating MLOps and governance ensures that these systems remain cost-effective, secure, and aligned with business innovation goals.
