Embedding the MLOps Lifecycle into OT Reference Models
This addresses integration problems for industrial practitioners using MLOps in OT environments, but it is incremental as it adapts existing models rather than introducing new paradigms.
The paper tackles the challenge of integrating Machine Learning Operations (MLOps) practices into Operational Technology (OT) systems by analyzing obstacles and proposing a systematic approach to embed MLOps into OT reference models like RAMI 4.0 and ISA-95, demonstrating through a real-world use case that structured adaptation can enable successful integration.
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.