A reference architecture for the operationalization of machine learning models in manufacturing

Raffin T, Reichenstein T, Werner J, Kühl A, Franke J (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Procedia CIRP Curran

Original Authors: Tim Raffin, Tobias Reichenstein, Jonas Werner, Alexander Kühl, Jörg Franke

Book Volume: 115

Pages Range: 130-135

DOI: 10.1016/j.procir.2022.10.062

Abstract

Inherent characteristics of machine learning algorithms such as their probabilistic nature, their reliance on large datasets as well as their need for constant retraining pose major challenges to the operationalization of machine learning models (MLOps) in the manufacturing domain. As such systems are known to quickly accumulate technical debt due to system-level interdependencies of code, data, and models, clear abstractions boundaries are mandatory. Therefore, this publication derives a systematic functional decomposition of an MLOps system tailored to the manufacturing industry into specific domains and contexts. Moreover, a concrete deployment view is provided, and possible future research directions are discussed.

Authors with CRIS profile

Tim Raffin Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Tobias Reichenstein Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Alexander Kühl Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

How to cite

APA:

Raffin, T., Reichenstein, T., Werner, J., Kühl, A., & Franke, J. (2022). A reference architecture for the operationalization of machine learning models in manufacturing. Procedia CIRP, 115, 130-135. https://dx.doi.org/10.1016/j.procir.2022.10.062

MLA:

Raffin, Tim, et al. "A reference architecture for the operationalization of machine learning models in manufacturing." Procedia CIRP 115 (2022): 130-135.

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