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.