Data Storage Architectures for Machine Learning and Artificial Intelligence
On-Premises and Public Cloud
By Enrico Signoretti, Senior Storage Analyst, GIGAOM
In this valuable report, Enrico Signoretti, Senior Storage Analyst, Gigaom, discusses the most recent storage architecture designs and innovative solutions deployed on-premises, cloud, and hybrid, aimed at supporting ML/AI workloads for enterprise organizations of all sizes.
Enterprise organizations are aware of the strategic value of ML/AI for their business and are increasing investments in this area.
End users are looking for turn-key solutions that are easy to implement and that deliver a quick ROI (Return on Investment).
Many of the solutions available are based on a two-tier architecture with a flash-based, parallel, and scale-out file system for active data processing and object storage for capacity and long term data retention. There are also some innovative solutions that are taking a different approach, with the two tiers integrated together in a single system.
Find out how Qumulo’s single-system architecture, which is easier to implement and manage, is a common choice for many enterprise organizations today.