Summary
This white paper discusses the role of object storage in enterprise AI, highlighting its evolution from a basic archival solution to a critical component of modern data infrastructure. It outlines how object storage meets the high-performance demands of AI workloads, particularly through innovations like RDMA acceleration, which provide low-latency and high-throughput access. The document details the AI workflow, which includes stages such as data preparation, model training, and inference, emphasizing the importance of efficient data management to handle large volumes of diverse data. It describes the steps involved in data preparation, including collection, preprocessing, and feature engineering, and explains how object storage systems can centralize and scale data effectively. Additionally, the paper addresses the significance of storage systems in model training and inference, noting the need for fast, scalable storage to support AI applications. Finally, it highlights the storage requirements across the AI lifecycle, emphasizing the need for adaptability in performance and capacity.
