
Publica
Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten. Automated machine learning for predictive quality in production
| Procedia CIRP 93 (2020), S.443-448 ISSN: 2212-8271 |
| Conference on Manufacturing Systems (CMS) <53, 2020, Online> |
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| Englisch |
| Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation |
| Fraunhofer IPT () |
Abstract
Applications that leverage the benefits of applying machine learning (ML) in production have been successfully realized. A fundamental hurdle to scale ML-based projects is the necessity of expertise from manufacturing and data science. One possible solution lies in automating the ML pipeline: integration, preparation, modeling and model deployment. This paper shows the possibilities and limits of applying AutoML in production, including a benchmarking of available systems. Furthermore, AutoML is compared to manual implementation in a predictive quality use case: AutoML still requires programming knowledge and is outperformed by manual implementation - but sufficient results are available in a shorter timespan.