Embedding the MLOps Lifecycle into OT Reference Models

TL;DR

This paper explores integrating MLOps practices into industrial OT systems by analyzing existing reference models and proposing a systematic adaptation approach, demonstrated through a real-world case study.

Contribution

It evaluates the suitability of RAMI 4.0 and ISA-95 for MLOps integration and provides a detailed mapping to facilitate structured adaptation in OT environments.

Findings

Standard MLOps practices require adaptation for OT environments.

Structured mapping to reference models enables successful MLOps integration.

Real-world case study validates the proposed approach.

Abstract

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.