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Challenges in the Deployment and Operation of Machine Learning in Practice

Baier, Lucas; Jöhren, Fabian; Seebacher, Stefan

Machine learning has recently emerged as a powerful technique to increase operational efficiency or to develop new value propositions. However, the translation of a prediction algorithm into an operationally usable machine learning model is a time-consuming and in various ways challenging task. In this work, we target to systematically elicit the challenges in deployment and operation to enable broader practical dissemination of machine learning applications. To this end, we first identify relevant challenges with a structured literature analysis. Subsequently, we conduct an interview study with machine learning practitioners across various industries, perform a qualitative content analysis, and identify challenges organized along three distinct categories as well as six overarching clusters. Eventually, results from both literature and interviews are evaluated with a comparative analysis. Key issues identified include auto- mated strategies for data drift detection and handling, standardization of machine learning infrastructure, and appropriate communication and expectation management.

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Preprint §
DOI: 10.5445/IR/1000095028
Veröffentlicht am 25.06.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator ISBN: 978-1-73363-250-8
KITopen-ID: 1000095028
Erschienen in ECIS 2019 proceedings . 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14, 2019. Research Papers
Veranstaltung 27th European Conference on Information Systems (ECIS 2019), Stockholm, Schweden, 08.06.2019 – 14.06.2019
Verlag AIS eLibrary (AISeL)
Seiten Paper: 163
Externe Relationen Abstract/Volltext
Nachgewiesen in Scopus
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