  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. To hypothesise, we
believe that organisations represented in the survey can be divided to three
categories -- (i) figuring out how to best use data; (ii) focusing on building
the first models and getting them to production; and (iii) managing several
models, their versions and training datasets, as well as retraining and
frequent deployment of retrained models. In the results, the majority of
respondents are in category (i) or (ii), focusing on data and models; however
the benefits of MLOps only emerge in category (iii) when there is a need for
frequent retraining and redeployment. Hence, setting up an MLOps pipeline is a
natural step to take, when an organization takes the step from ML as a
proof-of-concept to ML as a part of nominal activities.
