This paper presents a Spark-based modular LangGraph framework, designed to enhance machine learning workflows through scalability, visualization, and intelligent process optimization. At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark's distributed computing capabilities and integrates with LangGraph for workflow orchestration. Agent AI facilitates the automation of data preprocessing, feature engineering, and model evaluation while dynamically interacting with data through Spark SQL and DataFrame agents. Through LangGraph's graph-structured workflows, the agents execute complex tasks, adapt to new inputs, and provide real-time feedback, ensuring seamless decision-making and execution in distributed environments. This system simplifies machine learning processes by allowing users to visually design workflows, which are then converted into Spark-compatible code for high-performance execution. The framework also incorporates large language models through the LangChain ecosystem, enhancing interaction with unstructured data and enabling advanced data analysis. Experimental evaluations demonstrate significant improvements in process efficiency and scalability, as well as accurate data-driven decision-making in diverse application scenarios. This paper emphasizes the integration of Spark with intelligent agents and graph-based workflows to redefine the development and execution of machine learning tasks in big data environments, paving the way for scalable and user-friendly AI solutions.

翻译：本文提出了一种基于Spark的模块化LangGraph框架，旨在通过可扩展性、可视化和智能流程优化来增强机器学习工作流。该框架的核心创新是引入了智能体AI，这一关键创新充分利用了Spark的分布式计算能力，并与LangGraph集成实现工作流编排。智能体AI通过Spark SQL和DataFrame智能体动态交互数据，实现了数据预处理、特征工程和模型评估的自动化。借助LangGraph的图结构工作流，智能体能够执行复杂任务、适应新输入并提供实时反馈，确保分布式环境中决策与执行的无缝衔接。本系统允许用户可视化设计工作流，随后将其转换为Spark兼容代码进行高性能执行，从而简化了机器学习流程。该框架还通过LangChain生态系统整合了大语言模型，增强了对非结构化数据的交互能力，实现了高级数据分析。实验评估表明，该系统在多种应用场景中显著提升了流程效率与可扩展性，并实现了精准的数据驱动决策。本文重点阐述了Spark与智能体及图基工作流的集成，以重新定义大数据环境中机器学习任务的开发与执行方式，为可扩展且用户友好的AI解决方案开辟了新路径。