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insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3

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. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how 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.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 2

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. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how 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.

Pure Makes Customers “AI-First” Infrastructure a Reality

Pure Storage (NYSE: PSTG), a fast growing data storage company, announced a host of new and improved AI solutions that provide enterprise customers with the features and functionality needed to execute increasingly complex AI initiatives through any phase or scale. Built on Pure’s industry-leading file and object system, FlashBladeTM, and its joint AI-Ready Infrastructure (AIRITM) offering with NVIDIA, customers can develop and deploy AI rapidly to keep pace with modern business

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

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. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how 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.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

This new technology guide from DDN shows how 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. 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.

Advanced Performance and Massive Scaling Driven by AI and DL

In this contributed article, Kurt Kuckein, Director of Marketing for DDN Storage, discusses how current enterprise and research data center IT infrastructures are woefully inadequate in handling the demanding needs of AI and DL. Designed to handle modest workloads, minimal scalability, limited performance needs and small data volumes, these platforms are highly bottlenecked and lack the fundamental capabilities needed for AI-enabled deployments.

Building a Data Catalog: A Guide to Planning & Implementing

Building and implementing a data catalog can help your enterprises’ data community discover and use the best data and analytics resources for their projects. A data catalog can help businesses achieve faster results, and make better decisions. As for the next steps to address the importance of data catalogs in your business, Data.world covers that, as well, in a new report.

Transformative Solutions for Accelerating AI, Analytics and Deep Learning at NVIDIA #GTC19

One pivotal message received by attendees of this week’s NVIDIA GPU Technology Conference (GTC) in Silicon Valley is the importance of game-changing storage solutions and applications that empower users to accomplish their most challenging AI objectives.

How AI-optimized Hardware Solves Important Compute and Storage Requirements

Today we continue the insideBIGDATA Executive Round Up, our annual feature showcasing the insights of thought leaders on the state of the big data industry, and where it is headed. In today’s discussion, our panel of experienced big data executives – Ayush Parashar, Co-founder and Vice President of Engineering for Unifi Software, Robert Lee, Vice President & Chief Architect, Pure Storage, Inc., and Oliver Schabenberger, COO & CTO at SAS – discusses how AI-optimized hardware solves important compute and storage requirements in support of AI, machine learning, and deep learning.

Executive Spotlight: Robert Lee, Vice President & Chief Architect for Pure Storage

The insideBIGDATA 2019 Executive Round Up features insights from industry executives with lengthy experience in the big data industry. Here’s a look at the insights from Robert Lee, Vice President & Chief Architect for Pure Storage. Robert is focused on product architecture where he specifically looks at new innovation areas for Pure. Robert joined Pure in 2013 as part of the FlashBlade founding team and led the software architecture and development for the product.