As machine learning has become more widely adopted by businesses, neveropen set out to survey our audience to learn more about how companies approach this work. Do companies with more experience deploying machine learning in production use methods that differ significantly from organizations that are just beginning? For companies that haven’t begun this journey, are there any best practices that might help?
Looking at mainstream adoption for machine learning now—especially in light of recent data privacy legislation such as General Data Protection Regulation (GDPR) in Europe and a similar political movement in California—we wanted to probe current trends, with these questions in particular:
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- How experienced are companies with machine learning adoption, in terms of number of years deploying models in production?
- What has the impact been on culture and organization—for example, have job titles changed?
- Who builds machine learning models: internal teams, external consultants, cloud APIs?
- How are decisions and priorities set and by whom within the organization?
- What methodologies apply for developing machine learning—for example, Agile?
- What metrics are used to evaluate success?
Notable findings from the survey include the following:
- Job titles specific to machine learning are already widely used at organizations with extensive experience in machine learning: data scientist (81%), machine learning engineer (39%), deep learning engineer (20%).
- One in two (54%) respondents who belong to companies with extensive experience in machine learning check for fairness and bias. Overall, 40% of respondents indicated their organizations check for model fairness and bias. As tutorials and training materials become available, the number of companies capable of addressing fairness and bias should increase.
- One in two (53%) respondents who belong to companies with extensive experience in machine learning check for privacy (43% across respondents from all companies). The EU’s GDPR mandates “privacy-by-design” (“inclusion of data protection from the onset of the designing of systems rather than an addition”), which means more companies will add privacy to their machine learning checklist. Fortunately, new regulations coincide with the rise of tools and methods for privacy-preserving analytics and machine learning.
- One in two (51%) 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.
For a deep dive into these insights and more, download the free report The State of Machine Learning Adoption in the Enterprise with the full findings from our ML adoption survey.
