MLOps Challenges in Industry 4.0

Author:

Faubel LeonhardORCID,Schmid Klaus,Eichelberger Holger

Abstract

AbstractAn 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 only little research focusing on MLOps for Industry 4.0. In this paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We provide an initial analysis of MLOps approaches and 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.

Funder

Bundesministerium für Bildung und Forschung

Universität Hildesheim

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Machine Learning Operations Platform for Streamlined Model Serving in Industry 5.0;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06

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