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Accelerate Deep Learning with a Modern Storage Platform

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Although most analytics pipelines today use direct-attached storage (DAS) or distributed direct-attached storage (DDAS), allowing admins to utilize commodity off-the-shelf systems/components, the approach is still full of potential problems. Older systems are not optimized to analyze semi-structured and unstructured data often used in AI and deep learning.

As a result, legacy storage has become the primary bottleneck for AI workloads. This white paper delves further into why traditional storage can't meet deep learning needs, and demonstrates a flash-based architecture that is purpose-built for parallel workloads that are required for deep learning processing. Read on for more on how to deliver the data throughput needed for AI.

Vendor:
Pure Storage
Posted:
16 Aug 2018
Published:
16 Aug 2018
Format:
PDF
Length:
8 Page(s)
Type:
White Paper
Language:
English

This resource is no longer available.