将大模型与自动化MLOps流程融合，打造全流程智能开发环境。
SmartMLOps Studio: Design of an LLM-Integrated IDE with Automated MLOps Pipelines for Model Development and Monitoring
- 集成大模型助手，自动完成代码生成、调试建议和流水线配置。
- 在UCI Adult和M5数据集上，配置时间减少61%，可复现性提升45%。
- 适合需要高效建模与持续监控的机器学习工程师和研发团队。
人工智能与机器学习应用的快速发展，对统一模型开发、部署与监控的集成环境需求日益增长。传统IDE仅聚焦代码编写，缺乏对完整ML生命周期的智能支持；现有MLOps平台又与编码流程脱节。为此，本文提出一种集成大语言模型（LLM）的IDE设计，内嵌自动化的MLOps流水线，实现模型开发与监控的一体化。系统通过LLM助手提供代码生成、调试建议与自动流水线配置，后端集成自动化数据验证、特征存储、漂移检测、重训练触发及CI/CD部署编排。原型系统SmartMLOps Studio在UCI Adult和M5数据集的分类与预测任务上进行了评估，结果表明：相比传统工作流，流水线配置时间减少61%，实验可复现性提升45%，漂移检测准确率提高14%。该研究构建了AI工程新范式，将IDE从静态编码工具转变为动态、全生命周期感知的智能平台。
原文摘要 · Abstract (English)
The rapid expansion of artificial intelligence and machine learning (ML) applications has intensified the demand for integrated environments that unify model development, deployment, and monitoring. Traditional Integrated Development Environments (IDEs) focus primarily on code authoring, lacking intelligent support for the full ML lifecycle, while existing MLOps platforms remain detached from the coding workflow. To address this gap, this study proposes the design of an LLM-Integrated IDE with automated MLOps pipelines that enables continuous model development and monitoring within a single environment. The proposed system embeds a Large Language Model (LLM) assistant capable of code generation, debugging recommendation, and automatic pipeline configuration. The backend incorporates automated data validation, feature storage, drift detection, retraining triggers, and CI/CD deployment orchestration. This framework was implemented in a prototype named SmartMLOps Studio and evaluated using classification and forecasting tasks on the UCI Adult and M5 datasets. Experimental results demonstrate that SmartMLOps Studio reduces pipeline configuration time by 61%, improves experiment reproducibility by 45%, and increases drift detection accuracy by 14% compared to traditional workflows. By bridging intelligent code assistance and automated operational pipelines, this research establishes a novel paradigm for AI engineering - transforming the IDE from a static coding tool into a dynamic, lifecycle-aware intelligent platform for scalable and efficient model development.
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