Machine Learning Operations (MLOps) practices are increas- ingly adopted in industrial settings, yet their integration with Opera- tional Technology (OT) systems presents significant challenges. This pa- per analyzes the fundamental obstacles in combining MLOps with OT en- vironments and proposes a systematic approach to embed MLOps prac- tices into established OT reference models. We evaluate the suitability of the Reference Architectural Model for Industry 4.0 (RAMI 4.0) and the International Society of Automation Standard 95 (ISA-95) for MLOps integration and present a detailed mapping of MLOps lifecycle compo- nents to RAMI 4.0 exemplified by a real-world use case. Our findings demonstrate that while standard MLOps practices cannot be directly transplanted to OT environments, structured adaptation using existing reference models can provide a pathway for successful integration.

翻译：机器学习运维（MLOps）实践在工业环境中日益普及，但其与运营技术（OT）系统的集成仍面临重大挑战。本文分析了MLOps与OT环境结合的基本障碍，并提出将MLOps实践系统化嵌入现有OT参考模型的方法。我们评估了工业4.0参考架构模型（RAMI 4.0）和国际自动化协会标准95（ISA-95）对MLOps集成的适用性，并通过实际用例详细展示了MLOps生命周期组件与RAMI 4.0的映射关系。研究结果表明，虽然标准MLOps实践无法直接移植到OT环境，但基于现有参考模型的结构化适配可为成功集成提供可行路径。