A Case Study of the Challenges with Applying Machine Learning in Industry: A Software Engineering Perspective

Abstract

Data science is a growing trend and the advancement in machine learning and AI have been creating headlines in recent years. This has sparked an interest, not just in traditional IT-industries but also in businesses such as manufacturing, medicine and retail. Numerous industries are seeing potential in making their business more data driven and seeks to implement these trending technologies but few people know of the challenges that comes with applying it. This thesis aims at identifying the challenges, bridging the gap and lowering the entry barrier for engineers and researcher to contribute in the field of applied machine learning. In this case study, we examine how software engineers, data scientists and researchers can structure their work in order to increase the success rate of ML projects. Through interviews and a practical implementation test we analyze the underlying key concept that could help in bridging this gap. We conclude that software engineers can support in some initial data science activities, that communication between different stakeholders is crucial to the success of projects and that simpler ML models might be preferable in projects with time restrictions.