How can a file system enable faster deep learning for AI?

Robust data management is crucial as more companies invest in Big Data, analytics, and AI

How can a file system enable faster deep learning for AI?

Big Data adoption is currently experiencing a “strong upward trend”, according to a Dresner report. However, when a company possesses large sets of data it also requires reliable and scalable data infrastructure.

Becoming data-driven

Although large companies are investing huge amounts in Big Data and AI, 75% fear disruption from data-driven digital competitors. This is according to the Fortune 1000 C-level executives interviewed by NewVantage Partners in their 2019 survey.

Last year, 97% of executives said that their firms were investing in Big Data in order to become “nimble, data-driven” companies. In 2019, 55% of firms are investing over $50 million as they attempt to accelerate their business agility.

Despite this upward trend in investment, many firms reported issues in their journey to becoming data-driven. Among the 98% of respondents that aspired to become data-driven, just 32% have already done so.

Faster deep learning

Modern analytics platforms are “GPU intensive” and demand large data sets to deliver the highest levels of accuracy. These platforms also require a high bandwidth, low latency storage infrastructure, according to WekaIO.

As a result, this ensures that a GPU cluster is receives as much data as the application requires to function effectively. The WekaIO Matrix platform is “the world’s fastest and most scalable file system for these data intensive applications.”

The platform also has a “proven scalable performance of over 10GBytes”, whether hosted on premises or in the public cloud. As a result, this enables data-driven companies to derive valuable, faster insights from their data.

Advanced data protection

Over 69% of enterprises are reportedly re-evaluating their data protection strategy, according to an Evaluator Group report. WekaIO Matrix is reportedly “the only solution that provides end-to-end data management for data intensive AI and analytics workloads.”

In fact, a single global namespace spans from high performance flash for ingest and inference, to petabyte scaling to any S3 compatible object store. Whether on premises or in the public cloud, this solution enables long term data growth and preservation.

Administrators and users are also granted instant access to the corporate-wide data set under management. A patented data protection scheme also “distributes data across the entire file system,” and overall system reliability increases as the system scales.

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