TY  - CONF
A1  - Schuh, Günther
A1  - Stroh, Max-Ferdinand
A1  - Benning, Justus
A1  - Leachu, Stefan
A1  - Schmid, Katharina
T1  - Function Analysis For Selecting Automated Machine Learning Solutions
T2  - Proceedings of the Conference on Production Systems and Logistics: CPSL 2022.
N2  - 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.
KW  - Machine Learning
KW  - AI
KW  - Auto-ML
KW  - rev
Y1  - 2022
UR  - https://epub.fir.de/frontdoor/index/index/docId/1838
UR  - https://www.repo.uni-hannover.de/bitstream/handle/123456789/12264/Schuh2-CPSL2022.pdf?sequence=1&isAllowed=y
SP  - 359
EP  - 369
PB  - publish-Ing.
CY  - Hannover
ER  -