SmartMLOps Studio: Design of an LLM-Integrated IDE with Automated MLOps Pipelines for Model Development and Monitoring

• URL: http://arxiv.org/abs/2511.01850v1
• Date: Mon, 03 Nov 2025 18:56:59 GMT
• Title: SmartMLOps Studio: Design of an LLM-Integrated IDE with Automated MLOps Pipelines for Model Development and Monitoring
• Authors: Jiawei Jin, Yingxin Su, Xiaotong Zhu,
• Abstract summary: Traditional Integrated Development Environments (IDEs) focus primarily on code, lacking intelligent support for the full machine learning lifecycle.<n>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.<n>The backend incorporates automated data validation, feature storage, drift detection, retraining triggers, and CI/CD deployment orchestration.<n> Experimental results demonstrate that SmartMLOps Studio reduces pipeline configuration time by 61%, improves experiment by 45%, and increases drift detection accuracy by 14% compared to traditional.
• Score: 0.3823356975862005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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