SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines

2026-06-12

AI Summary

This work addresses the cumbersome and error-prone nature of data preparation and feature engineering in machine learning pipelines, as well as the limited controllability of current large language model (LLM)-assisted programming approaches, which hinders their deployment in production systems. The authors propose SemPipes, a declarative, LLM-driven semantic operator programming model that seamlessly integrates natural language instructions with Python code and automatically synthesizes dataset- and context-aware implementations during training. By innovatively combining declarative semantic operators with evolutionary search optimization, SemPipes enables controlled and optimizable integration of LLMs into ML pipelines. The accompanying SemPiper interactive interface allows users to edit pipelines, inspect generated code, and observe operator synthesis and optimization in real time, demonstrating the approach’s flexibility and practical utility.

Abstract

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.

Problem

• machine learning pipelines
• code synthesis
• semantic operators
• large language models
• pipeline development

Innovation

• semantic operators
• code synthesis
• declarative programming
• LLM integration
• ML pipeline optimization