Industrial MLOps: a systematic review of architectures and implementation challenges

Abstract

The rise of advanced digitalization in Industry 4.0 has enabled manufacturers to leverage data through AI and ML solutions for various manufacturing challenges. However, integrating these models into factory settings remains challenging, as models that perform well on static datasets struggle with dynamic shop floor data. MLOps is an emerging discipline focused on bridging the gap between ML models and production environments; however, in the manufacturing domain, questions remain about how to effectively deploy ML models using MLOps. This article addresses these gaps by conducting a systematic literature review combined with thematic analysis to explore architectures and frameworks used to adopt MLOps in real-world industrial applications, referred to here as industrial MLOps. The study identifies key architectural requirements and outlines seven implementation challenges, with recommendations and architecture mappings to overcome them. Results show that fully automated MLOps frameworks remain underdeveloped, and that modular, scalable architectures are recommended to address model drift, data quality, and integration challenges.