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.
Balaban, Ebru Elif; Murtada, Yasser; Walker, Dustin; Jawwad, Jumana; and Rossmann, Alexander, "Theoretical Foundation, Effectiveness, and Design Artefact for Machine Learning Service Repositories" (2022). PACIS 2022 Proceedings. 251.
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