Buyer's Handbook:

Evaluate your machine learning and AI data storage requirements

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Storage options meet the needs of machine learning and AI

Organizations are using machine learning and AI to get insights they can use to improve how they do business. But these workloads differ from regular ones, as they require large amounts of data to build and train statistical models.

All this data must also be processed and stored: Active data must be moved to a high-performance platform for processing, and other data often gets transferred to long-term storage. To meet these requirements, some storage vendors offer either converged infrastructure products or products that organizations can build into their machine learning and AI projects. These tools package storage, networking and compute or scale-out file storage with GPUs. Other vendors work with partners to deliver their products as prepackaged appliances.

To help identify the storage product that will best meet your organization's AI workload needs, it's important to understand which features and functionality are most important. Evaluate features based on how your organization will be performing machine learning and AI tasks. Key considerations include scalability, high-performance capabilities and overall cost.  

This buyer's guide examines products from nine industry-leading storage vendors and newer startups. A comprehensive product roundup compares numerous features and functionality, including capacity, performance and the file sizes supported.