  Data scientists often develop machine learning models to solve a variety of
problems in the industry and academy but not without facing several challenges
in terms of Model Development. The problems regarding Machine Learning
Development involves the fact that such professionals do not realize that they
usually perform ad-hoc practices that could be improved by the adoption of
activities presented in the Software Engineering Development Lifecycle. Of
course, since machine learning systems are different from traditional Software
systems, some differences in their respective development processes are to be
expected. In this context, this paper is an effort to investigate the
challenges and practices that emerge during the development of ML models from
the software engineering perspective by focusing on understanding how software
developers could benefit from applying or adapting the traditional software
engineering process to the Machine Learning workflow.
