Hybrid and Dedicated Clouds to Help Drive Massive Growth for Machine Learning Projects Over Next Two Years

Grazed from Univa

Machine learning (ML) is poised for explosive growth over the next two years with an increasing number of projects moving into production by 2020, based on a recent survey of more than 344 technology and IT professionals titled "The Future of Machine Learning," conducted by technology marketing organization, Dimensional Research. Though a diverse set of ML projects are currently initiated by 93% of the respondents, only 22% of these projects have actually moved into production, citing migration as the top technical challenge.

Univa, the leading innovator in on-premise and hybrid cloud workload management solutions for enterprise HPC customers, sponsored the survey that polled 344 technology and IT professionals across the globe and across 17 industries, with technology, financial services and healthcare leading the charge in ML adoption. "Our customers are already asking for guidance with migrating their HPC and machine learning workloads to the cloud or hybrid environment," said Rob Lalonde, vice president and general manager of Navops at Univa. "As a result, we decided to conduct this survey to better understand the type of projects driving value in machine learning, as well as better assess what key challenges users are currently facing that are preventing them from moving their projects into production. We look forward to utilizing this data to help guide our customers and recommend the right set of tools and migration options needed to accelerate ML value."

The Future of Machine Learning - It's All About Cloud Migration and Tools

Survey participants highlighted some key components driving their ability to successfully move ML projects into production:

  • 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.
  • Nearly 9 out of 10 companies surveyed expect to use GPUs as part of their ML infrastructure.
  • More than 80% of respondents plan to use hybrid cloud for ML projects while keeping costs down.

Cloud Migration is Critical

Though 69% of companies surveyed have three or more teams requesting ML projects, only 2 in 10 companies have ML projects running in production. The biggest technical challenges cited with current ML projects include the migration of workloads, data and applications. Yet experts surveyed expect the number of tools used for running ML projects to increase with Amazon and Microsoft benefiting from increased market share.

"We see a tremendous opportunity to help our customers move their ML projects into production," added Lalonde. "This survey revealed that there are a diverse number of projects for ML learning, indicating numerous areas of value. We look forward to working with our customers to help them fully utilize and scale these projects and resources across their on-premise, hybrid and cloud infrastructures."

To view the full report of "The Future of Machine Learning," visit the following link.