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?
New & Notable
Machine-learning storage News
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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.
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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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October 24, 2019
24
Oct'19
New NVMe IBM storage for Spectrum Scale cuts install time
IBM introduces new NVMe-based flash storage system bundled with Spectrum Scale, enables Spectrum Discover to access backup date, and updates pay-as-you-grow pricing.
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July 02, 2019
02
Jul'19
Open source machine learning accelerates winemaking
Using open source machine learning, Palmaz Vineyards developed a tool to automatically alert winemakers of environmental conditions. Soon, the data flowed like wine.
Machine-learning storage Get Started
Bring yourself up to speed with our introductory content
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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
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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
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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
Evaluate Machine-learning storage Vendors & Products
Weigh the pros and cons of technologies, products and projects you are considering.
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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
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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
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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
Manage Machine-learning storage
Learn to apply best practices and optimize your operations.
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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
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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
Problem Solve Machine-learning storage Issues
We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.
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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
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Machine learning for data storage optimizes data analysis