Following continuous software engineering practices, there has been an increasing interest in rapid deployment of
machine learning (ML) features, called MLOps. In this paper, we
study the importance of MLOps in the context of data scientists’
daily activities, based on a survey where we collected responses
from 331 professionals from 63 different countries in ML domain,
indicating on what they were working on in the last three months.
Based on the results, up to 40% respondents say that they
work with both models and infrastructure; the majority of the
work revolves around relational and time series data; and the
largest categories of problems to be solved are predictive analysis,
time series data, and computer vision. The biggest perceived
problems revolve around data, although there is some awareness
of problems related to deploying models to production and
related procedures.