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O'Reilly Survey: The State of Enterprise Machine Learning Adoption

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Global Survey Reveals Key Findings from Machine Learning Deployments
and Identifies Where Companies Should Focus as they Begin their Machine
Learning Journey

O'Reilly,
the premier source for insight-driven learning on technology and
business, today announced its latest survey findings in the report, "The
State of Machine Learning Adoption in the Enterprise
." As machine
learning has become more widely adopted across industries, O'Reilly set
out to learn more about how companies approach this work.

By surveying more than 11,000 data specialists across North America,
Europe, and Asia, the company has identified some of the key learnings
that derive from deploying machine learning in production, and where
other companies should focus as they begin their journey of machine
learning adoption.

Notable findings from the survey include:

  • Job titles specific to machine learning are already widely used at
    organizations with extensive machine learning experience: data
    scientist (81%), machine learning engineer (39%), deep learning
    engineer (20%).
  • 54% of respondents who belong to companies with extensive experience
    in machine learning check for fairness and bias (compared to 40%
    across all respondents).
  • More than half (53%) of respondents who work for companies with
    extensive experience in machine learning check for privacy (43% across
    all respondents). The EU's GDPR
    mandates "privacy-by-design," which means more companies will continue
    to add privacy to their machine learning checklist.
  • 51% of respondents use internal data science teams to build their
    machine learning models, whereas use of AutoML services from cloud
    providers is in low single digits, and this split grows even more
    pronounced among sophisticated teams. Companies with less-extensive
    experience tend to rely on external consultants.
  • Sophisticated teams tend to have data science leads set team
    priorities and determine key metrics for project success –
    responsibilities that would typically be performed by product managers
    in more traditional software engineering.

"Navigating large-scale machine learning deployments is no easy feat,
especially in light of recent privacy legislation such as GDPR. This
research gives organizations a better understanding of how other
companies are approaching machine learning at all stages of adoption and
how the technology is impacting these companies from a cultural and
organizational perspective," said Ben Lorica, O'Reilly chief data
scientist and Strata Data Conference chair.

Full survey results can be downloaded from the O'Reilly
website
. Further exploring some of the most important trends and
developments in machine learning, O'Reilly and Cloudera will host the
upcoming Strata
Data Conference
taking place September 11-13 in New York City.

Conference registration
is now open, and a limited number of media
passes
are available for qualified journalists and analysts. For
questions regarding media credentials, please contact maureen@oreilly.com;
for media queries or scheduling interviews, contact OReilly@famapr.com.
Follow @StrataConf
or #StrataData
on Twitter for the latest news and updates.

About O'Reilly
For almost 40 years, O'Reilly has provided
technology and business training, knowledge, and insight to help
companies succeed. Our unique network of experts and innovators share
their knowledge and expertise through the company's Safari training and
learning platform and at O'Reilly conferences. As a SaaS learning
platform, Safari delivers highly topical and comprehensive technology
and business learning solutions to millions of users across enterprise,
consumer, and university channels. For more information, visit www.oreilly.com.

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