dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/12264 |
|
dc.identifier.uri |
https://doi.org/10.15488/12166 |
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dc.contributor.author |
Schuh, Günther
|
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dc.contributor.author |
Stroh, Max-Ferdinand
|
|
dc.contributor.author |
Benning, Justus
|
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dc.contributor.author |
Leachu, Stefan
|
|
dc.contributor.author |
Schmid, Katharina
|
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dc.contributor.editor |
Herberger, David
|
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dc.contributor.editor |
Hübner, Marco
|
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dc.date.accessioned |
2022-06-02T11:44:49Z |
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dc.date.issued |
2022 |
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dc.identifier.citation |
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: https://doi.org/10.15488/12166 |
|
dc.identifier.citation |
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: https://doi.org/10.15488/12166 |
|
dc.description.abstract |
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. |
eng |
dc.language.iso |
eng |
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dc.publisher |
Hannover : publish-Ing. |
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dc.relation.ispartof |
Proceedings of the Conference on Production Systems and Logistics: CPSL 2022 |
|
dc.relation.ispartof |
https://doi.org/10.15488/12314 |
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dc.rights |
CC BY 3.0 DE |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/3.0/de/ |
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dc.subject |
Machine learning |
eng |
dc.subject |
Auto-ML |
eng |
dc.subject |
Artificial Intelligence |
eng |
dc.subject |
Function Analysis |
eng |
dc.subject |
Digitalization |
eng |
dc.subject |
Konferenzschrift |
ger |
dc.subject.ddc |
620 | Ingenieurwissenschaften und Maschinenbau
|
|
dc.title |
Function Analysis for Selecting Automated Machine Learning Solutions |
eng |
dc.type |
BookPart |
|
dc.type |
Text |
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dc.relation.essn |
2701-6277 |
|
dc.bibliographicCitation.firstPage |
359 |
|
dc.bibliographicCitation.lastPage |
369 |
|
dc.description.version |
publishedVersion |
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tib.accessRights |
frei zug�nglich |
|