Hot-to-Cold Data Management: Infrastructure Requirements in the Age of AI

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Presented by

Jordan Winkelman

About this talk

This rise of AI, deep learning, and modern demanding workflows has changed storage and infrastructure requirements. Organizations must have powerful solutions that can fuel demanding AI and data science workloads (hot data) on the front end, integrated with low-cost, secure private clouds to archive the massive amounts of data (cold data) to train these models on the back end—all while keeping data protected and secure. Data continually flows along the hot-to-cold continuum, and the infrastructure supporting this lifecycle must be flexible, automated, and simple to manage to keep up with the ever-changing nature of this data. Learn how AI is evolving the landscape of storage and infrastructure requirements, and the considerations for storing and managing hot and cold data to fuel AI initiatives.
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Quantum delivers end-to-end data management solutions designed for the AI era. From high-performance ingest that powers AI applications and demanding data-intensive workloads, to massive, durable data lakes to fuel AI models, Quantum delivers the most comprehensive and cost-efficient solutions. Leading organizations in life sciences, government, media and entertainment, research, and industrial technology trust Quantum with their most valuable asset – their data. Quantum is listed on Nasdaq (QMCO). For more information visit www.quantum.com.