用对话式AI让普通人也能轻松操作复杂机器学习流程。
Towards Conversational AI for Human-Machine Collaborative MLOps
- 构建可扩展的智能体系统，通过自然语言管理机器学习流水线。
- 集成KFP、MinIO和RAG Agent，支持数据、流程与知识协同管理。
- 适合非技术背景用户快速上手，降低复杂MLOps平台使用门槛。
本文提出一种基于大语言模型（LLM）的对话式智能体系统，旨在提升人机在机器学习运维（MLOps）中的协作效率。我们设计了Swarm Agent架构，通过集成专用智能体实现自然语言驱动的ML工作流创建与管理。该系统采用分层模块化设计，包含用于流水线编排的KubeFlow Pipelines（KFP）Agent、用于数据管理的MinIO Agent，以及用于领域知识融合的检索增强生成（RAG）Agent。通过迭代推理循环与上下文感知处理，系统使不同技术水平的用户可通过直观对话界面完成流水线执行与监控、数据与制品管理、文档查询等操作。本方法有效缓解了Kubeflow等复杂MLOps平台的可用性瓶颈，使先进机器学习工具对更广泛人群可及，同时保持向其他平台扩展的灵活性。论文详细描述了系统架构与实现，并验证其显著降低复杂度、降低入门门槛的效果。
原文摘要 · Abstract (English)
This paper presents a Large Language Model (LLM) based conversational agent system designed to enhance human-machine collaboration in Machine Learning Operations (MLOps). We introduce the Swarm Agent, an extensible architecture that integrates specialized agents to create and manage ML workflows through natural language interactions. The system leverages a hierarchical, modular design incorporating a KubeFlow Pipelines (KFP) Agent for ML pipeline orchestration, a MinIO Agent for data management, and a Retrieval-Augmented Generation (RAG) Agent for domain-specific knowledge integration. Through iterative reasoning loops and context-aware processing, the system enables users with varying technical backgrounds to discover, execute, and monitor ML pipelines; manage datasets and artifacts; and access relevant documentation, all via intuitive conversational interfaces. Our approach addresses the accessibility gap in complex MLOps platforms like Kubeflow, making advanced ML tools broadly accessible while maintaining the flexibility to extend to other platforms. The paper describes the architecture, implementation details, and demonstrates how this conversational MLOps assistant reduces complexity and lowers barriers to entry for users across diverse technical skill levels.
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