MLOps Challenges in Industry 4.0

被引:0
作者
Faubel L. [1 ]
Schmid K. [1 ]
Eichelberger H. [1 ]
机构
[1] Software Systems Engineering, Institute of Computer Science, University of Hildesheim, Universitätspl. 1, Lower Saxony, Hildesheim
关键词
Challenges; Industry; 4.0; Machine learning; MLOps;
D O I
10.1007/s42979-023-02282-2
中图分类号
学科分类号
摘要
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 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. © 2023, The Author(s).
引用
收藏
相关论文
共 42 条
  • [31] Tamburri D.A., Sustainable mlops: Trends and challenges, . In: 22Nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 17-23, (2020)
  • [32] Borg M., Jabangwe R., Aberg S., Ekblom A., Hedlund L., Lidfeldt A., Test automation with grad-CAM heatmaps—a future pipe segment in MLOps for vision AI?, In: International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, pp. 175-181, (2021)
  • [33] Sun Z., Sandoval L., Crystal-Ornelas R., Mousavi S.M., Wang J., Lin C., Cristea N., Tong D., Carande W.H., Ma X., Rao Y., Bednar J.A., Tan A., Wang J., Purushotham S., Gill T.E., Chastang J., Howard D., Holt B., Gangodagamage C., Zhao P., Rivas P., Chester Z., Orduz J., John A., A review of earth artificial intelligence, Comput Geosci, (2022)
  • [34] Akinosho T.D., Oyedele L.O., Bilal M., Barrera-Animas A.Y., Gbadamosi A.-Q., Olawale O.A., A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways, Ecol Inform, (2022)
  • [35] Yasser A., Abu-Elkhier M., Towards fluid software architectures: Bidirectional human-AI interaction, In: 2021 36Th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 1368-1372, (2021)
  • [36] Fernando H., Marshall J., What lies beneath: material classification for autonomous excavators using proprioceptive force sensing and machine learning, Autom Constr, (2020)
  • [37] Faubel L., Schmid K., Eichelberger H., Is MLOps different in Industry 4.0? General and specific challenges, Proceedings of the 3Rd International Conference on Innovative Intelligent Industrial Production and Logistics—IN4PL, pp. 161-167
  • [38] Sato D., Wider A., Windheuser C., Continuous Delivery for Machine Learning. Visited, pp. 2022-2106, (2019)
  • [39] Hannelius T., Salmenpera M., Kuikka S., Roadmap to adopting OPC UA, 2008 6th IEEE International Conference on Industrial Informatics, pp. 756-761, (2008)
  • [40] Inigo M.A., Porto A., Kremer B., Perez A., Larrinaga F., Cuenca J., Towards an asset administration shell scenario: A use case for interoperability and standardization in industry 4.0, Network Operations and Management Symposium, pp. 1-6, (2020)