insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

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Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources.

Traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-
art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

Optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. Optimized storage is designed for the needs of a broad range of problem domains including FinTech, life sciences, design, HPC, government, smart cities, media, energy, and many more.

This new guide from DDN includes the following sections:

  • Introduction
  • How Optimized Storage Solves AI Challenges
  • Frameworks for AI and DL Workflows
  • Partners Important Role for Leading-Edge Case Studies
  • Summary

The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.

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