MLOps: A Multiple Case Study in Industry 4.0
Leonhard Faubel, Klaus Schmid
TL;DR
The paper examines how MLOps is practically implemented in Industry 4.0 through a multiple-case study of three European companies with mature practices. It uses semi-structured interviews to characterize organizational structures, tools, and deployment processes, revealing both variations and common patterns across industries. Four industrial MLOps scenarios are identified, and a three-layer view—organization, development, production—along with concrete software-implementation insights is presented. The study highlights emergent challenges in human-machine interaction, data access standardization, and explainability, and discusses implications for building adaptable, standards-driven MLOps platforms in manufacturing. Overall, the work provides empirical guidance for practitioners and a foundation for future cross-domain MLOps research in Industry 4.0.
Abstract
As Machine Learning (ML) becomes more prevalent in Industry 4.0, there is a growing need to understand how systematic approaches to bringing ML into production can be practically implemented in industrial environments. Here, MLOps comes into play. MLOps refers to the processes, tools, and organizational structures used to develop, test, deploy, and manage ML models reliably and efficiently. However, there is currently a lack of information on the practical implementation of MLOps in industrial enterprises. To address this issue, we conducted a multiple case study on MLOps in three large companies with dedicated MLOps teams, using established tools and well-defined model deployment processes in the Industry 4.0 environment. This study describes four of the companies' Industry 4.0 scenarios and provides relevant insights into their implementation and the challenges they faced in numerous projects. Further, we discuss MLOps processes, procedures, technologies, as well as contextual variations among companies.