Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Machine Learning - Managerial Perspective: A Study to define concepts and highlight challenges in a product-based IT Organization
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The purpose of this research is to understand the main managerial challenges that arise in the context of Machine Learning. This research aims to explore the core concepts of Machine Learning and provide the same conceptual foundation to managers to overcome possible obstacles while implementing Machine Learning. Therefore, the main research question is: 

What are the phases and the main challenges while managing Machine Learning project in a product based IT organization? 

 The focus is on the main concepts of Machine Learning and identifying challenges during each phase through literature review and qualitative data collected from interviews conducted with professionals. The research aims to position itself in the field of research which looks for inputs from consultants and management professionals either associated with Machine Learning or they are planning to start such initiatives. In this research paper we introduce ACDDT (Agile-Customer-Data-Domain-Technology) model framework for managers. This framework is centered on the main challenges in Machine Learning project phases while dealing with customer, data, domain and technology. In addition, the frame work also provides key inputs to managers for managing those challenges and possibly overcome them.

Place, publisher, year, edition, pages
2019. , p. 73
Keywords [en]
Machine Learning, Management Challenges, Model Management, Team, Decision Making, Technology, Domain Expert, Customer, ACDDT Model
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:bth-18835OAI: oai:DiVA.org:bth-18835DiVA, id: diva2:1367105
Subject / course
IY2594 Magisterarbete MBA
Educational program
IYABA MBA programme
Presentation
2019-05-28, Virtual, skype meeting, Malmo, 15:16 (English)
Supervisors
Examiners
Available from: 2019-11-26 Created: 2019-10-31 Last updated: 2019-11-26Bibliographically approved

Open Access in DiVA

Machine Learning(1447 kB)1120 downloads
File information
File name FULLTEXT01.pdfFile size 1447 kBChecksum SHA-512
c712952d10458e9fd2acfe53acc16f0f630765767275b3232c67fefca82d8f7c37acc2e44c98ab002331e10e5fe516f1929a921d598853656249e100ad61e4c7
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Bangabash, Subhasish
By organisation
Department of Industrial Economics
Other Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 1121 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 562 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf