MLOps for Cyber-Physical Production Systems
Challenges and Solutions
Leonhard Faubel (Universität Hildesheim)
Thomas Woudsma (Prodrive Technologies)
Benjamin Kloepper (Capgemini)
Holger Eichelberger (Universität Hildesheim)
Fabian Buelow (ABB Corporate Research, Heidelberg)
Klaus Schmid (Universität Hildesheim)
Amir Ghorbani Ghezeljehmeidan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Leila Methnani (Umeå University)
Andreas Theodorou (Universitat Politecnica de Catalunya)
Magnus Bang (Linköping University)
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Abstract
Machine Learning Operations (MLOps) involves software development practices for Machine Learning (ML), including data management, preprocessing, model training, deployment, and monitoring. While MLOps have received significant interest, much less work has been published addressing MLOps in industrial production settings lately, particularly if solutions are not cloud-based. This article addresses this shortcoming based on our and our partner’s real industrial experience in various projects. While there is a broad range of challenges for MLOps in cyber-physical production systems (CPPS), we focus on those related to data, models, and operations as we assume these will directly benefit the reader and provide solutions such as lightweight integration, integration of domain knowledge, periodic calibration, and interactive interfaces. In this way, we want to support practitioners in setting up industrial MLOps environments in CPPS. Further, we discuss explainability as an additional part of MLOps, which should be explored in more detail in the future.
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