MLOps Pipeline Generation for Reinforcement Learning: A Low-Code Approach Using Large Language Models: Replication Package

Title: MLOps Pipeline Generation for Reinforcement Learning: A Low-Code Approach Using Large Language Models: Replication Package Authors: Stephen John Warnett; Evangelos Ntentos; Uwe Zdun About: This is the replication package artefact for the paper entitled "MLOps Pipeline Generation for Reinforcement Learning: A Low-Code Approach Using Large Language Models". Paper Abstract: MLOps (Machine Learning Operations) and its application to Reinforcement Learning (RL) involve various challenges when integrating Machine Learning and RL models into production systems, entailing considerable expertise and manual effort, which can be error-prone and obstruct scalability and rapid deployment. We propose a new approach to address these challenges in generating MLOps pipelines. We present a low-code, template-based approach leveraging Large Language Models (LLMs) to automate RL pipeline generation, validation and deployment. In our approach, the Pipes and Filters pattern allows for the fine-grained generation of MLOps pipeline configuration files. Built-in error detection and correction help maintain high-quality output standards. To empirically evaluate our solution, we assess the correctness of pipelines generated with seven LLMs for three open-source RL projects. Our initial approach achieved an average error rate of 0.187 across all seven LLMs. OpenAI GPT-4o performed the best with an error rate of just 0.09, followed by Qwen2.5 Coder with an error rate of 0.15. We implemented a single round of improvements to our implementation and low-code template. We reevaluated our solution on the best-performing LLM from the initial evaluation, achieving perfect results with an overall error rate of zero for OpenAI GPT-4o. Our findings indicate that pipelines generated by our approach have low error rates, potentially enabling rapid scaling and deployment of reliable MLOps for RL pipelines, particularly for practitioners lacking advanced software engineering or DevOps skills. Our approach contributes towards demonstrating increased reliability and trustworthiness in LLM-based solutions, despite the uncertainty hitherto associated with LLMs.