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Identifying Pitfalls in Machine Learning Implementation Projects: A Case Study of Four Technology-Intensive Organizations
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Marketing and Entrepreneurship.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Identifiering av fallgropar i implementationsprojekt inom maskininlärning : En fallstudie av fyra teknologi-intensiva organisationer (Swedish)
Abstract [en]

This thesis undertook the investigation of finding often occurring mistakes and problems that organizations face when conducting machine learning implementation projects. Machine learning is a technology with the strength of providing insights from large amounts of data. This business value generating technology has been defined to be in a stage of inflated expectations which potentially will cause organizations problems when doing implementation projects without previous knowledge. By a literature review and hypothesis formation followed by interviews with a sample group of companies, three conclusions are drawn from the results. First, indications show there is a correlation between an overestimation of the opportunities of machine learning and how much experience an organization has within the area. Second, it is concluded that data related pitfalls, such as not having enough data, low quality of the data, or biased data, are the most severe. Last, it is shown that realizing the value of long-term solutions regarding machine learning projects is difficult, although the ability increases with experience.

Place, publisher, year, edition, pages
2018. , p. 53
Series
TRITA-ITM-EX ; 2018:224
Keywords [en]
artificial intelligence, machine learning, pitfalls, implementation, project management
Keywords [sv]
artificiell intelligens, AI, maskininlärning, fallgropar, implementation, projektledning
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-231830OAI: oai:DiVA.org:kth-231830DiVA, id: diva2:1230169
External cooperation
Findwise AB
Subject / course
Computer Engineering with Industrial Economy
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Presentation
2018-05-31, Lindstedtsvägen 30, Stockholm, 09:07 (Swedish)
Supervisors
Examiners
Available from: 2018-07-26 Created: 2018-07-03 Last updated: 2018-07-26Bibliographically approved

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CiteExportLink to record
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