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New Survey Sees Explosive Growth in ML Projects Over the Next 2 Years

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New Survey Sees Explosive Growth in ML Projects Over the Next 2 Years

Take a look at a survey thats has shown explosive growth in Machine Learning projects over the next two years.

· AI Zone ·
Free Resource

This summer, Univa sponsored an industry-wide survey in conjunction with Dimensional Research to better understand what key challenges our HPC users are currently facing that are preventing them from moving their machine learning (ML) projects into production. Our goal is to use this data to help guide our customers and recommend the right set of tools and migration options needed to accelerate value in Machine Learning. With this in mind, this survey interviewed 344 technology and IT professionals worldwide and across 17 industries. Some key findings from this survey include:

  1. There is a direct correlation between HPC and ML, with more than 88% of respondents indicating that they are working with HPC in their jobs.
  2. Nearly 9 out of 10 companies surveyed expect to use GPUs as part of their ML infrastructure, which is consistent with what we have seen in our customer base.
  3. More than 80% of respondents plan to use hybrid cloud for ML projects while keeping costs down. The Univa team has seen GPUs as drivers for moving HPC workloads to the cloud to access expensive resources, which is illustrated in our recent case study with The Wharton School.
  4. Though 69% of companies surveyed have three or more teams requesting ML projects, only 2 in 10 companies have ML projects running in production, citing migration of workloads as their biggest technical challenge.
  5. Of the 17 industries interviewed in this survey, technology, financial services, and healthcare are clearly leading the charge when it comes to ML adoption.

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(Machine_Learning_Infographics_pdf)

These results reveal that Machine Learning is certainly poised for an explosive growth phase, with nearly every company anticipating more projects over the next two years — driven by numerous stakeholder groups within the company. Furthermore, there is a diverse set of ML projects that companies have initiated, showing there are several areas where ML can drive value. Yet currently, just 2 in 10 companies have ML projects running in production, citing cloud migration as the top technical challenge. As a result, this survey indicated to the Univa team that HPC users can benefit from the right guidance to help them fully utilize and scale these projects and resources across their on-premise, hybrid, and cloud infrastructures.

For more details about this research and to access the complete report on the “The Future of Machine Learning,” conducted by Dimensional Research, visit the following link.

Topics:
machine learning ,high performance computing ,hybrid cloud ,ai

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