  Context: Machine Learning Operations (MLOps) has emerged as a set of
practices that combines development, testing, and operations to deploy and
maintain machine learning applications. Objective: In this paper, we assess the
benefits and limitations of using the MLOps principles in online supervised
learning. Method: We conducted two focus group sessions on the benefits and
limitations of applying MLOps principles for online machine learning
applications with six experienced machine learning developers. Results: The
focus group revealed that machine learning developers see many benefits of
using MLOps principles but also that these do not apply to all the projects
they worked on. According to experts, this investment tends to pay off for
larger applications with continuous deployment that require well-prepared
automated processes. However, for initial versions of machine learning
applications, the effort taken to implement the principles could enlarge the
project's scope and increase the time needed to deploy a first version to
production. The discussion brought up that most of the benefits are related to
avoiding error-prone manual steps, enabling to restore the application to a
previous state, and having a robust continuous automated deployment pipeline.
Conclusions: It is important to balance the trade-offs of investing time and
effort in implementing the MLOps principles considering the scope and needs of
the project, favoring such investments for larger applications with continuous
model deployment requirements.
