MLOps: A Multiple Case Study in Industry 4.0

TL;DR

This paper presents a detailed case study of MLOps implementation in three large Industry 4.0 companies, highlighting practical challenges, processes, and organizational aspects of deploying ML models in industrial settings.

Contribution

It provides empirical insights into how MLOps is practically implemented in Industry 4.0 environments through multiple case studies, filling a gap in real-world application knowledge.

Findings

Identified key challenges faced during MLOps deployment.

Described organizational and technological practices in industrial MLOps.

Highlighted variations in MLOps processes across companies.

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.