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Meeting the Challenges of Deep Learning Workloads

As artificial intelligence comes of age, organizations are investing capital and human resources — in a big way. A report by the Enterprise Strategy Group (ESG) found that 59 percent of responding organizations expect to significantly increase their spending on AI and machine learning in 2019.1

Findings like these suggest it’s now “full speed ahead” for AI in the enterprise. And to put the AI pedal to the metal, organizations need to deploy tested and optimized infrastructure stacks that are built for the scale and challenges of machine and deep learning.

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