SELM: Software Engineering of Machine Learning Models
This addresses the challenge of optimizing machine learning processes for interdisciplinary teams, though it appears incremental in combining existing software engineering approaches with ML.
The paper tackles the problem of improving machine learning model development by introducing the SELM framework, which applies software engineering principles to engineer concepts, resulting in claimed improvements in efficiency, accuracy, and resource usage with less hardware and smaller datasets.
One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning.