Publikation - Einzelansicht
details for the publication
| author(s) | Leonhard Faubel, Thomas Woudsma, Benjamin Klöpper, Holger Eichelberger, Bülow. Fabian, Klaus Schmid, Amir Ghorbani Ghezeljehmeidan, Leila Methnani, Andreas Theodorou and Magnus Bang |
| title | MLOps for Cyber-Physical Production Systems: Challenges and Solutions |
| publication type | Beitrag zu Zeitung oder Zeitschrift |
| journal | IEEE Software |
| year | 2024 |
| digital object identifier (doi) | DOI: 10.1109/MS.2024.3441101 |
| 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 |
