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The work introduces a digital, human-centered assistance system that combines human and artificial intelligence in interactive guidance. It lets production and development employees apply data mining and ML methods without programming on high-dimensional, nonstationary, imbalanced tabular data. 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Möllensiep, Dennis; Kuhlenkötter, Bernd; Möller, Matthias  \nArticle — Published Version  \nML Pro: digital assistance system for interactive machine learning in production  \nJournal of Intelligent Manufacturing  \nProvided in Cooperation with:  \nSpringer Nature  \nSuggested Citation: Neunzig, Christian; Möllensiep, Dennis; Kuhlenkötter, Bernd; Möller, Matthias (2023) : ML Pro: digital assistance system for interactive machine learning in production, Journal of Intelligent Manufacturing, ISSN 1572-8145, Springer US, New York, NY, Vol. 35, Iss. 7, pp. 3479-3499,  \n[https://doi.org/10.1007/s10845-023-02214-0](https://doi.org/10.1007/s10845-023-02214-0)  \nThis Version is available at:  \n[https://hdl.handle.net/10419/317749](https://hdl.handle.net/10419/317749)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \n[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)  \nML Pro: digital assistance system for interactive machine learning in production  \nChristian Neunzig1,2 · Dennis Möllensiep1 · Bernd Kuhlenkötter1 · Matthias Möller2  \nReceived: 18 April 2023 / Accepted: 5 September 2023 / Published online: 4 October 2023 © The Author(s) 2023  \nAbstract  \nThe application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.  \nKeywords Failure prognosis · Predictive quality control · Supervised learning · Human–machine interaction · Interactive machine learning  \nAbbreviations  \nAI Artificial intelligence  \nAPI Application programming interface  \nANN A","cbCairvVE2hjQGv0","https://ap.wps.com/l/cbCairvVE2hjQGv0","pdf",2417516,22,"English","# Abstract\n# Keywords\n# Abbreviations\n# Introduction","[{\"question\":\"What additional value does ML Pro provide compared with existing tools?\",\"answer\":\"It allows classical shallow and deep learning as well as generative and oversampling methods to be interactively applied across feature-table use cases for different user modes without programming.\"}]","ML Pro - digital assistance system for interactive machine learning in production | PDF",55]