A Case Study of the Challenges with Applying Machine Learning in Industry: A Software Engineering Perspective
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Typ
Examensarbete för masterexamen
Program
Software engineering and technology (MPSOF), MSc
Publicerad
2019
Författare
Eksmo, Samuel
Liu, Hanyan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
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Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Computer , science , computer science , engineering , project , thesis , Machine learning , software engineering , anomaly detection