Towards a Reference Architecture for Machine Learning Operations

Industrial organisations increasingly rely on machine learning (ML) to improve quality, maintenance, and planning in Industry 4.0/5.0 ecosystems. However, turning experimental models into reliable services on the production floor remains complex due to the heterogeneity of operational technologies (OTs) and information technologies (ITs), including implementation constraints, latency in edge-fog-cloud scenarios, governance requirements, and continuous performance degradation caused by data drift. Although Machine Learning Operations (MLOps) provides lifecycle practices for deployment, monitoring, and retraining, the evidence is fragmented across tool-centric descriptions, case-specific pipelines, and conceptual architectures, offering limited guidance on which industrial constraints should inform architectural decisions and how to evaluate solutions. This work addresses that gap through a PRISMA-guided systematic review of 49 studies on industrial MLOps (with the search and screening primarily targeting Industry 4.0/IIoT operationalisation contexts, as reflected in the search strategy and corpus) and an evidence-based synthesis of principles, challenges, lifecycle practices, and enabling technologies. From this synthesis, industrial requirements are derived that encompass OT/IT integration, edge-fog-cloud orchestration, security and traceability, and observability-based lifecycle control. On this basis, a reference architecture is proposed that maps these requirements to functional layers, data and control flows, and verifiable responsibilities. To support reproducibility and practical inspectability, the article also presents an open-source architectural instantiation aligned with the proposed decomposition. Finally, the evaluation is illustrated through a predictive maintenance use case (tool breakage) in a single CNC machining cell, where the objective is to demonstrate end-to-end feasibility under realistic operational constraints rather than cross-scenario superiority or broad industrial generalisability.