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While the use of machine learning (ML) in production started near the turn of the century, it’s taken roughly 20 years for the practice to become mainstream throughout industry. With this report, you’ll learn how more than 11,000 data specialists responded to a recent O’Reilly survey about their organization’s approach—or intended approach—to machine learning.
Data scientists, machine learning engineers, and deep learning engineers throughout the world answered detailed questions about their organization’s level of ML adoption. About half of the respondents work for enterprises in the early stages of exploring ML, while the rest have moderate or extensive experience deploying ML models to production.
This survey reveals:
- How much experience the respondents’ organizations have with ML, such as number of years deploying models in production
- What ML’s impact has been on their companies’ cultures and organization
- How many enterprises build their ML models using internal teams, external consultants, or cloud APIs
- How decisions and priorities related to ML are set within the organization—and by whom
- What methodologies companies have used to develop ML, such as Agile
- What metrics they’ve used to evaluate success with machine learning
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