Function Analysis For Selecting Automated Machine Learning Solutions
- 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.
Verfasserangaben: | Günther SchuhORCiDGND, Max-Ferdinand Stroh, Justus Benning, Stefan Leachu, Katharina Schmid |
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URL: | https://www.repo.uni-hannover.de/bitstream/handle/123456789/12264/Schuh2-CPSL2022.pdf?sequence=1&isAllowed=y |
DOI: | https://doi.org/10.15488/12166 |
Titel des übergeordneten Werkes (Englisch): | Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. |
Verlag: | publish-Ing. |
Ort: | Hannover |
Dokumentart: | Konferenzveröffentlichung |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 20.05.2022 |
Datum der Erstveröffentlichung: | 20.05.2022 |
Datum der Freischaltung: | 17.01.2023 |
Freies Schlagwort / Tag: | rev AI; Auto-ML; Machine Learning |
Erste Seite: | 359 |
Letzte Seite: | 369 |
FIR-Nummer: | SV7597 |
Konferenzname: | Conference on Production Systems and Logistics (CPSL 2022) |
Konferenzort: | Vancouver, Canada |
Konferenzzeitraum: | 17.05.2022-20.05.2022 |
Institut / Bereiche des FIR: | FIR e. V. an der RWTH Aachen |
Informationsmanagement | |
DDC-Klassifikation: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften |