Machine-learning storage

Find the latest news and tips for machine-learning storage on this topics page. Learn how machine-learning data storage can impact your data center, both from the amounts of data generated by machine-learning applications and in the roles machines can play in data mining and storage management. Are the days of self-drive storage arrays far off?

June 30, 2021 30 Jun'21
Nvidia finalizes GPUDirect Storage 1.0 to accelerate AI, HPC

Nvidia GPUDirect Storage's driver hit 1.0 status to enable direct memory access between GPU and storage and boost the performance of data-intensive AI, HPC and analytics workloads.
May 11, 2021 11 May'21
Dell EMC PowerScale adds all-NVMe, Nvidia GPUDirect options

A Dell EMC PowerScale update adds a high-end all-NVMe F900 model with Nvidia GPUDirect support for AI and analytics workloads plus a OneFS 9.2 upgrade to improve performance.
December 10, 2020 10 Dec'20
Seagate, Western Digital outline progress on RISC-V designs

Seagate and Western Digital provide updates on their RISC-V-enabled processor designs that can drive advances in data storage and security at the network's edge.
November 07, 2019 07 Nov'19
New API lets Azure Stack connect to Scality object storage

New Azure Blob API will enable Microsoft Azure Stack Hub and Edge hybrid and private cloud customers to use scale-out Scality Ring object storage on premises.

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6 key AI data storage questions answered

Want to know what to watch for when planning storage for AI workloads? Find out the key considerations and challenges for dealing with the complexities of AI. Continue Reading
QLC NAND

QLC NAND (quad-level cell NAND) is a form of NAND flash memory that can store up to four bits of data per memory cell. Continue Reading
8 factors that make AI storage more efficient

AI workloads need storage optimized for performance, capacity and availability. Discover everything you'll want to consider when planning storage for AI applications. Continue Reading

Weigh the pros and cons of technologies, products and projects you are considering.

Composable disaggregated infrastructure right for advanced workloads

Composability provides the agility, speed and efficient resource utilization required to support advanced workloads that continue to grow ever-more sophisticated and comprehensive. Continue Reading
Infrastructure for machine learning, AI requirements, examples

AI, machine and deep learning infrastructure has component and configuration requirements. See what hardware you need and how it goes together in an HCI or high-density system. Continue Reading
How AI is changing the storage consumption landscape

Deep learning, machine learning and neural networks are drastically changing how storage is consumed. Find out why this is happening and what products are helping meet this need. Continue Reading

Learn to apply best practices and optimize your operations.

Storage optimization strategies for time-series data stores

Storing time-series data should depend on how the data is used and its age. Discover tips for saving data for comparative analysis, machine learning and other purposes. Continue Reading
Data center storage continues to evolve

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Machine learning for data analytics can solve big data storage issues

Discover how AI and machine learning -- with support from major vendors and technologies like Lambda architecture, FPGAs and containers -- address big data analytics challenges. Continue Reading

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High-performance object storage challenges in the modern data center

Object storage works well for long-term backup and archiving. See how high-performance capabilities are expanding it to high-scale, high-capacity workloads. Continue Reading
Machine learning for data storage optimizes data analysis

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