Machine learning (ML) pipelines require extensive data preparation, feature engineering, and integration across heterogeneous sources, making them tedious and error-prone to develop. While large language models (LLMs) have recently shown promise for assisting programming tasks, chat-based interfaces provide limited control over pipeline behavior and often produce code that is difficult to optimize or integrate into production systems. We demonstrate SemPipes, a novel programming model that extends ML pipelines with declarative, LLM-powered semantic data operators. SemPipes allows developers to specify high-level natural language instructions for data-centric operations, while seamlessly combining these operators with arbitrary Python code from standard data science libraries. For the semantic operators, it synthesizes specialized implementations at pipeline training time, conditioned on dataset characteristics and pipeline context, enabling the flexible yet controlled integration of LLM capabilities. We demonstrate SemPipes through SemPiper, an interactive interface that visualizes computational graphs of the pipelines, synthesized operator implementations, and optimization trajectories produced by an evolutionary search procedure. Attendees can explore three end-to-end scenarios, modify pipelines, inspect generated code, and observe how semantic operators are synthesized and iteratively optimized. The demonstration highlights how declarative semantic operators enable controllable, optimizable, and practical integration of LLMs into ML pipeline development.

翻译：机器学习（ML）管道需要跨异构来源进行广泛的数据准备、特征工程和集成，导致其开发过程繁琐且易出错。虽然大语言模型（LLM）近期在辅助编程任务方面展现出潜力，但基于聊天的界面难以精确控制管道行为，且生成的代码往往难以优化或集成到生产系统。我们提出SemPipes这一新型编程模型，它通过声明式且基于LLM的语义数据运算符扩展了机器学习管道。SemPipes允许开发者使用高级自然语言指令指定数据驱动操作，同时将这些运算符与标准数据科学库中的任意Python代码无缝结合。针对语义运算符，该模型在管道训练阶段根据数据集特征和管道上下文合成专门的实现，从而实现灵活但可控的LLM能力集成。通过SemPiper交互式界面，我们展示了SemPipes的核心功能：该界面可可视化管道的计算图、合成后的运算符实现，以及由进化搜索过程产生的优化轨迹。参会者可以探索三个端到端场景，修改管道、检查生成的代码，并观察语义运算符如何被合成与迭代优化。该演示突显了声明式语义运算符如何实现可控、可优化且实用的LLM集成到ML管道开发中。