Related papers: Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

URL: http://arxiv.org/abs/2102.07574v1
Date: Mon, 15 Feb 2021 14:25:13 GMT
Title: Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review
Authors: Giuliano Lorenzoni and Paulo Alencar and Nathalia Nascimento and Donald Cowan
Abstract summary: Data scientists often develop machine learning models to solve a variety of problems in the industry and academy. This paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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