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English(EN) Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

新的LLM编排多智能体框架增强BDaaS生命周期自动化

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新的大数据即服务（BDaaS）框架，该框架利用由中央LLM编排的多智能体系统。该系统旨在自动化和提高从摄取到部署后监控和漂移检测的整个数据工程和MLOps生命周期的可靠性。与现有的单智能体或仅AutoML方法相比，所提出的架构将任务分解为专门的智能体，增强了生命周期级别的编排、工件治理和人工监督。 AI

影响 该框架可以简化和提高复杂数据管道的可靠性，有可能加速生产环境中AI模型的部署和维护。

排序理由 该集群包含一篇详细介绍数据工程和MLOps新框架的研究论文。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Aueaphum Aueawatthanaphisut, Badri Raj Lamichhane ·

    Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

    arXiv:2606.17915v1 Announce Type: cross Abstract: Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring. However, existing LLM-based data science ag…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Badri Raj Lamichhane ·

    Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization

    Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring. However, existing LLM-based data science agents and AutoML systems mainly focus on isolated w…

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