Function Analysis for Selecting Automated Machine Learning Solutions

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Schuh, G.; Stroh, M.-F.; Benning, J.; Leachu, S.; Schmid, K.: Function Analysis for Selecting Automated Machine Learning Solutions. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. Hannover : publish-Ing., 2022, S. 359-369. DOI:

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Sum total of downloads: 209

Methods of machine learning (ML) are notoriously difficult for enterprises to employ productively. Data science is not a core skill of most companies, and acquiring external talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratising machine learning by introducing elements such as low-code / no-code functionalities into its model creation process. Multiple applications are possible for Auto-ML, such as Natural Language Processing (NLP), predictive modelling and optimization. However, employing Auto-ML still proves difficult for companies due to the dynamic vendor market: The solutions vary in scope and functionality while providers do little to delineate their offerings from related solutions like industrial IoT-Platforms. Additionally, the current research on Auto-ML focuses on mathematical optimization of the underlying algorithms, with diminishing returns for end users. The aim of this paper is to provide an overview over available, user-friendly ML technology through a descriptive model of the functions of current Auto-ML solutions. The model was created based on case studies of available solutions and an analysis of relevant literature. This method yielded a comprehensive function tree for Auto-ML solutions along with a methodology to update the descriptive model in case the dynamic provider market changes. Thus, the paper catalyses the use of ML in companies by providing companies and stakeholders with a framework to assess the functional scope of Auto-ML solutions.
License of this version: CC BY 3.0 DE
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Proceedings CPSL 2022
Proceedings CPSL 2022

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pos. country downloads
total perc.
1 image of flag of Germany Germany 112 53.59%
2 image of flag of United States United States 23 11.00%
3 image of flag of Pakistan Pakistan 11 5.26%
4 image of flag of Russian Federation Russian Federation 5 2.39%
5 image of flag of India India 5 2.39%
6 image of flag of Indonesia Indonesia 5 2.39%
7 image of flag of China China 5 2.39%
8 image of flag of Portugal Portugal 4 1.91%
9 image of flag of Japan Japan 3 1.44%
10 image of flag of Austria Austria 3 1.44%
    other countries 33 15.79%

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