Data management is one of the most important aspects of designing an end-to-end AI solution, in addition to factors like your AI infrastructure stack and AI algorithms. Data for AI can be multimodal, varying from small files like documents to very large objects like videos—and storage systems need to be able to handle it all. In an AI pipeline, multiple types of storage workloads exist with different capacity as well as different latency and throughput requirements, depending on the AI phase in the pipeline. In this first part of our guide to storage for AI, we’ll dive into the storage…

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