Qualitative assessment of the impact of manufacturing-specific influences on Machine Learning Operations

Raffin T, Reichenstein T, Klier D, Kühl A, Franke J (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Original Authors: Tim Raffin, Tobias Reichenstein, Dennis Klier, Alexander Kühl, Jörg Franke

Book Volume: 115

Pages Range: 136-141

DOI: 10.1016/j.procir.2022.10.063

Abstract

Machine Learning Operations (MLOps) enables the streamlining of the development and deployment processes of machine learning models; thus, manufacturers can utilize the inherent flexibility and adaptability of Deep Learning at scale to further optimize their processes. This publication provides insights into the challenges that companies face while striving for the efficient operationalization of machine learning algorithms. Moreover, a mapping of capabilities and requirements is presented to provide a baseline for the qualitative analysis of the current state of the art. In conclusion, this article discusses the shortcomings of the existing literature and provides novel implications for MLOps systems in manufacturing.

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How to cite

APA:

Raffin, T., Reichenstein, T., Klier, D., Kühl, A., & Franke, J. (2022). Qualitative assessment of the impact of manufacturing-specific influences on Machine Learning Operations. Procedia CIRP, 115, 136-141. https://dx.doi.org/10.1016/j.procir.2022.10.063

MLA:

Raffin, Tim, et al. "Qualitative assessment of the impact of manufacturing-specific influences on Machine Learning Operations." Procedia CIRP 115 (2022): 136-141.

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