TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Balaban, Ebru
A1  - Murtada, Yasser
A1  - Walker, Dustin
A1  - Jawwad, Jumana
A1  - Rossmann, Alexander
T1  - Theoretical foundation, effectiveness, and design artefact for machine learning service repositories
T2  - PACIS 2022 Proceedings, 5-9 July 2022, Taipei/Sydney virtual conference
N2  - Machine learning (ML) has played an important role in research in recent years. For companies that want to use ML, finding the algorithms and models that fit for their business is tedious. A review of the available literature on this problem indicates only a few research papers. Given this gap, the aim of this paper is to design an effective and easy-to-use ML service repository. The corresponding research is based on a multi-vocal literature analysis combined with design science research, addressing three research questions: (1) How is current white and gray literature on ML services structured with respect to repositories? (2) Which features are relevant for an effective ML service repository? (3) How is a prototype for an effective ML service repository conceptualized? Findings are relevant for the explanation of user acceptance of ML repositories. This is essential for corporate practice in order to create and use ML repositories effectively.
KW  - design science
KW  - machine learning
KW  - repository
Y1  - 2022
UR  - https://aisel.aisnet.org/pacis2022/251/
SN  - 978-1-958200-01-8
SB  - 978-1-958200-01-8
SP  - 1
EP  - 17
S1  - 17
PB  - Association for Information Systems
CY  - Atlanta, GA
ER  -