Towards Industry 5.0: AAS/MLOps-Driven Model Maintenance for Data-Centric Production

Despite the advancements brought by digitalization across industries, only a few state-of-the-art data-driven methods successfully transition to production and remain viable. The sheer volume of physical assets in production lines, combined with constantly evolving requirements, makes model deployment and maintenance highly complex. This paper presents a production-ready architecture developed for data-driven digital assets at ABB Schaffhausen AG. The solution integrates MLOps best practices orchestrated via MLRun with the industry-standard metadata modeling system, Asset Administration Shell (AAS). We demonstrate how controlled artifact generation from MLRun facilitates experiment tracking and knowledge sharing while AAS ensures standardization and long-term maintenance. By combining MLOps and AAS, we effectively manage the ever-growing artifacts of data-driven solutions. Additionally, we explore how controlled artifact generation enables role-based MLOps by restricting access to re levant information based on industrial roles. This architecture supports a smooth transition to Industry 5.0.