将机器学习运维流程嵌入工业系统参考模型，解决落地难题
Embedding the MLOps Lifecycle into OT Reference Models
- 用现有工业参考模型映射MLOps全流程
- 实证显示标准MLOps无法直接移植到工业场景
- 适合智能制造与工业AI系统设计者参考
机器学习运维（MLOps）在工业领域应用日益广泛，但其与操作技术（OT）系统的集成仍面临重大挑战。本文分析了将MLOps融入OT环境的根本障碍，提出一种将MLOps实践系统性嵌入成熟OT参考模型的方法。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）在支持MLOps集成方面的适用性，并以真实案例展示了MLOps生命周期组件在RAMI 4.0中的详细映射。研究发现，尽管标准MLOps流程无法直接迁移到OT环境，但借助现有参考模型进行结构化适配，可为成功集成提供可行路径。
原文摘要 · Abstract (English)
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.
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