The problems regarding Machine LearningDevelopment involves the fact that such professionals do not realize that they perform ad-hoc practices that could be improved by the adoption of the Software Engineering Development Lifecycle . Ofcourse, since machine learning systems are different from traditional Softwaresystems, some differences in their respective development processes are to beexpected . 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 softwaredevelopers could benefit from applying or adapting the traditional software engineering process to the Machine Learning . workflow .

Author(s) : Giuliano Lorenzoni, Paulo Alencar, Nathalia Nascimento, Donald Cowan

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Keywords : engineering - machine - development - software - learning -

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