Inside Advanced Scale Challenges|Monday, December 10, 2018
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Few ML Projects Moving to Production, Survey Finds 

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The confluence of accelerators like cloud GPUs along with the ability to handle data-rich HPC workloads will help push more machine learning projects into production, concludes a new study that also stresses the importance of cloud migration and accompanying tools.

The survey released this week by workload management specialist Univa Corp. confirms the rush to machine learning. However, it also found a lack of workloads in production. The reason, according to a survey of 344 IT managers is lingering problems with cloud migration, including workloads, data and applications.

While the vast majority of respondents (93 percent) said they have machine learning projects underway, only 22 percent have been able to move them to production.

“Our customers are already asking for guidance with migrating their HPC and machine learning workloads to the cloud or hybrid environment,” said Rob Lalonde, a Univa vice president.

Read the full story here at sister web site Datanami.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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