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Quantum: Storage Architecture Considerations for Machine Learning at Scale
DescriptionThe fields of AI and machine learning are at the forefront of driving innovation and discoveries across the globe. These fields generate massive datasets that require extremely fast processing, continuous analytics, as well as long term protection and preservation. These requirements put pressure on traditional storage infrastructures, which is driving many research institutions to leverage new technologies including GPUs, NVMe, erasure-encoded object storage, and more. In this twenty minute presentation, we will outline key considerations for machine learning at scale, provide a reference architecture based on what we are seeing our customers deploy, and compare and contrast different options for long term preservation of machine learning data.