Embedding the MLOps Lifecycle into OT Reference Models

• URL: http://arxiv.org/abs/2510.20590v1
• Date: Thu, 23 Oct 2025 14:14:21 GMT
• Title: Embedding the MLOps Lifecycle into OT Reference Models
• Abstract summary: Machine Learning Operations (MLOps) practices are increas- ingly adopted in industrial settings, yet their integration with Opera- tional Technology (OT) systems presents significant challenges.<n>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.
• Score: 0.40831821256838124
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review [4.513916758263758]
  We conduct a gray literature review of 103 web sources to analyze state-of-practice knowledge on MLOps model integration and deployment.<n>We apply thematic analysis to synthesize these practices into recommended guidelines.<n>Our results serve as an overview of state-of-practice MLOps guidelines to support researchers and practitioners with the integration and deployment of ML models in their MLOps systems.
  arXiv  Detail & Related papers  (2026-06-03T21:09:50Z)
• Learning POMDP World Models from Observations with Language-Model Priors [52.11151769796862]
  Partially-observable Markov decision processes (POMDPs) provide a flexible modeling class for internal world models.<n>We ask whether language-model priors can reduce costly interaction by leveraging prior knowledge.<n>We show that emphPinductor matches the performance and sample efficiency of LLM-based POMDP learning methods.
  arXiv  Detail & Related papers  (2026-05-13T16:18:15Z)
• When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems [1.609940380983903]
  We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires replacement.<n>Key contribution is a Bayesian statistical approach that calibrates automated evaluation metrics against human judgments.<n>We demonstrate this framework on a commercial question-answering system serving 5.3M monthly interactions across six global regions.
  arXiv  Detail & Related papers  (2026-04-29T18:22:50Z)
• Automated Standardization of Legacy Biomedical Metadata Using an Ontology-Constrained LLM Agent [0.1105171275481129]
  We present an automated standardization system for biomedical metadata.<n>The system queries authoritative terminology services in real time to retrieve canonically correct vocabulary terms on demand.<n>Our evaluation shows that augmenting the LLM with real-time tool access consistently improves prediction accuracy.
  arXiv  Detail & Related papers  (2026-03-10T18:47:30Z)
• Adaptive Global and Fine-Grained Perceptual Fusion for MLLM Embeddings Compatible with Hard Negative Amplification [49.109117617514066]
  Multimodal embeddings serve as a bridge for aligning vision and language.<n>We propose Adaptive Global and Fine-grained perceptual Fusion for MLLM Embeddings.<n>AGFF-Embed comprehensively achieves state-of-the-art performance in both general and fine-grained understanding.
  arXiv  Detail & Related papers  (2026-02-05T14:52:35Z)
• Model-Driven Legacy System Modernization at Scale [0.0]
  This report presents a model-driven approach to legacy system modernization.<n>The four-stage process of analysis, enrichment, synthesis, and transition systematically extracts, abstracts, and transforms system artifacts.<n>We show that core user interface components and page structures can be migrated semi-automatically to a modern web stack while preserving functional behavior and essential non-functional qualities.
  arXiv  Detail & Related papers  (2026-02-04T09:07:20Z)
• Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
  In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
  arXiv  Detail & Related papers  (2026-02-04T05:37:09Z)
• From monoliths to modules: Decomposing transducers for efficient world modelling [74.41506965793417]
  We develop a framework for decomposing complex world models represented by transducers.<n>Our results clarify how to invert this process, deriving sub-transducers operating on distinct input-output subspaces.
  arXiv  Detail & Related papers  (2025-12-01T20:37:43Z)
• MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models [76.72220653705679]
  We introduce MCPEval, an open-source framework that automates end-to-end task generation and deep evaluation of intelligent agents.<n> MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines.<n> Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance.
  arXiv  Detail & Related papers  (2025-07-17T05:46:27Z)
• Large Language Models for Automating Clinical Data Standardization: HL7 FHIR Use Case [0.2516393111664279]
  We introduce a semi-automated approach to convert structured clinical datasets into HL7 FHIR format.<n>In an initial benchmark, resource identification achieved a perfect F1-score, with GPT-4o outperforming Llama 3.2.<n>Error analysis revealed occasional hallucinations of non-existent attributes and mismatches in granularity, which more detailed prompts can mitigate.
  arXiv  Detail & Related papers  (2025-07-03T17:32:57Z)
• Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing [0.12289361708127876]
  We present an alternative approach based on the recently introduced Model Context Protocol (MCP)<n>MCP allows systems to expose functionality through a standardized interface that is directly consumable by LLM-based agents.
  arXiv  Detail & Related papers  (2025-06-12T13:02:16Z)
• MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering [57.156093929365255]
  Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents.<n>MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios.<n>Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-12T17:35:43Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers [4.835091081509403]
  This paper introduces a unified MLOps lifecycle framework, further incorporating Large Language Model Operations (LLMOps)<n>We outline key roles, tools, and costs associated with MLOps adoption at various maturity levels.<n>By providing a standardized framework, we aim to help organizations clearly define and allocate the resources needed to implement MLOps effectively.
  arXiv  Detail & Related papers  (2025-03-19T13:20:14Z)
• RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration [2.879328762187361]
  We present RAAD-LLM, a novel framework for adaptive anomaly detection.<n>By effectively utilizing domain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time series data.<n>Results show significant improvements over our previous model with an accuracy increase from 70.7% to 88.6% on the real-world dataset.
  arXiv  Detail & Related papers  (2025-03-04T17:20:43Z)
• IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts [28.9807389592324]
  Large language model (LLM) agents have emerged as a promising solution to automate the workflow of machine learning.<n>We introduce Iterative Refinement, a novel strategy for LLM-driven ML pipeline design inspired by how human ML experts iteratively refine models.<n>By systematically updating individual components based on real training feedback, Iterative Refinement improves overall model performance.
  arXiv  Detail & Related papers  (2025-02-25T01:52:37Z)
• Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework [15.11130742093296]
  We propose a general LLM-agent-based modeling framework for transportation systems.<n>Our conceptual framework design closely replicates the decision-making and interaction processes and traits of human travelers.<n>Although further refinement of the LLM-agent-based modeling framework is necessary, we believe that this approach has the potential to improve transportation system modeling and simulation.
  arXiv  Detail & Related papers  (2024-12-09T17:24:41Z)
• Recursive Learning of Asymptotic Variational Objectives [49.69399307452126]
  General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. Online sequential IWAE (OSIWAE) allows for online learning of both model parameters and a Markovian recognition model for inferring latent states. This approach is more theoretically well-founded than recently proposed online variational SMC methods.
  arXiv  Detail & Related papers  (2024-11-04T16:12:37Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.