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.
- Agile is the methodology most widely used with ML work.
- 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.