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How flash storage is pitching in to help AI and machine learning

Slow, complex legacy storage systems have been unable to cope with modern data volume and velocity, but that’s changing with AI and machine learning supported by effective flash storage.

Jeff Rowe | May 08, 2018 11:23 am

Artificial intelligence (AI) is making rapid inroads into healthcare systems across the country and around the world, but it’s not going to happen successfully without organizations having the necessary data storage capacity.

That’s according to James Petter, EMEA VP at Pure Storage, who in a recent column for TradeArabia noted “it is critical that organizations . . .  carefully consider the infrastructure needed to support their AI ambitions. To innovate and improve AI algorithms, storage has to deliver uncompromised performance across all manner of access patterns—small to large files, random to sequential, and low to high concurrency—all with the ability to easily scale linearly and non-disruptively in order to grow capacity and performance.”

Consider, for example, the challenge faced by just one system on one day.  At Geisinger Health, 60,000 patient notes are created daily, and “there is no way humans could review all of them to identify the relatively few cases that describe insightful patient information for improving patient diagnostics.”

As Petter observes, “data can easily end up in infrastructure siloes at each stage of the AI pipeline—comprised of ingest, clean and transform, explore, train—making projects more time intensive, complex and inflexible.

“Bringing together data into a single centralized data storage hub as part of a deep learning architecture enables far more efficient access to information, increasing the productivity of data scientists and making scaling and operating simpler and more agile for the data architect. Modern all-flash based data platforms are ideal candidates to act as that central data hub. It’s the only storage technology capable of underpinning and releasing the full potential of projects operating in environments that demand high performance compute capabilities such as AI and deep learning.”

In Petter’s view, flash storage arrays are best suited for AI projects “as they encompass a parallelism that mimics the human brain, and enables multiple queries or jobs to run simultaneously. By building this type of flash technology into the very foundation of AI projects, it vastly improves the rate at which AI and ML initiatives can develop.”

Of course, not all healthcare organizations are diving into AI, but it is a tool all organizations should be looking at using to bring efficiency and accuracy to their data-heavy projects.  Moreover, what it relies on is storage that avoids bottlenecks and provides the data fast enough to feed the data analysis pipelines.

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