|author(s)||Leonhard Faubel, Klaus Schmid and Holger Eichelberger|
|title||Is MLOps different in Industry 4.0? General and Specific Challenges|
|publication type||Beitrag zu einer Tagung / Konferenz|
|booktitle||3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL)|
|digital object identifier (doi)||DOI: 10.5220/0011589600003329|
An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is no specialized research for Industry 4.0 in this regard. In this position paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.
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