RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0: Replication Package

Title: RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0: Replication Package Authors: Stephen John Warnett; Uwe Zdun About: This is the replication package artefact for the paper entitled "RLOps Pipeline Development with Low-Code and Large Language Models for Industry 4.0". Paper Abstract: Machine learning operations (MLOps) automate common tasks throughout the entire life cycle of a machine learning model. In Industry 4.0 environments, cyber-physical production systems increasingly utilise reinforcement learning (RL) models to optimise and automate production processes, giving rise to reinforcement learning operations (RLOps). However, manually configuring RLOps pipelines requires specialised development operations (DevOps) knowledge, and this can limit the potential for automation. We propose a template-based approach for rapidly creating and deploying RLOps pipelines in Industry 4.0 environments that minimises the required programming effort and expertise. Based on the Pipes and Filters pattern, our modular solution leverages large language models (LLMs) for automated pipeline creation. It enables fully automated execution, including model training, testing and deployment, with built-in quality control to ensure correct configurations. We validate our approach through evaluation with multiple LLMs in an Industry 4.0 context. Our results demonstrate that our solution, used with a suitable LLM, can reliably generate and execute RLOps pipelines with low error rates, thereby reducing development time and the need for specialised DevOps knowledge.标题：面向工业4.0的低代码与大语言模型（Large Language Model，LLM）RLOps流水线开发：复现包 作者：Stephen John Warnett；Uwe Zdun 简介：本数据集为题为"面向工业4.0的低代码与大语言模型RLOps流水线开发"的论文的复现包制品。 论文摘要：机器学习运维（Machine Learning Operations, MLOps）可实现机器学习模型全生命周期内常见任务的自动化。在工业4.0场景中，信息物理生产系统日益广泛地应用强化学习（Reinforcement Learning, RL）模型以优化并自动化生产流程，由此催生了强化学习运维（Reinforcement Learning Operations, RLOps）。然而，手动配置RLOps流水线需要具备专门的开发运维（Development Operations, DevOps）知识，这会限制自动化的应用潜力。本文提出一种基于模板的方法，可在工业4.0场景中快速创建并部署RLOps流水线，最大限度降低所需的编程工作量与专业技能要求。该模块化解决方案基于管道与过滤器模式（Pipes and Filters Pattern），借助大语言模型实现流水线的自动化创建。其支持模型训练、测试与部署的全自动化执行，并内置质量控制机制以确保配置的正确性。本文通过在工业4.0场景下对多款大语言模型进行评估验证了所提方法的有效性。实验结果表明，搭配合适的大语言模型时，本方案可可靠地生成并执行低错误率的RLOps流水线，从而缩短开发时长并降低对专门DevOps知识的需求。