The use of the term ‘data management’ is subject to varying interpretations between data professionals and storage providers, leading to some confusion when discussing the scope of services. Solidigm highlights the unique storage requirements for AI, as Karen Lopez wrote, emphasizing that AI servers need significantly more capacity and have specific data demands across different stages: Data ingest, Data prep, Training, Checkpointing, and Inference. As data professionals, our understanding of these distinct data workloads enables us to contribute valuably to discussions on data architecture, especially in collaboration with companies like Supermicro, who integrate these storage solutions into their AI server offerings.
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Storage for AI: Data Professional Overview
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