Machine learning engineering agents address pipeline data challenges
ML Engineering Agents - A Data Management Perspective
DatabasesMachine LearningSoftware Engineering
Summary
Machine learning engineering agents help automate creating complete machine learning workflows by writing and improving code step-by-step. The authors explain how these agents use language models to generate code and performance feedback to improve it. They point out that current evaluations often ignore real-world data challenges like diverse data sources and pipeline reliability. The paper highlights open problems where data management techniques can help improve these agents. They also provide practical guidance through interactive sessions with existing agents.
What this means in practice
- •For data engineering teams: Build automated ML pipelines that handle diverse and complex data sources with improved code generation and data management integration.
- •For enterprise ai compliance officers: Monitor and enforce fairness and compliance in automated machine learning workflows using insights from pipeline debugging and evaluation.
A position paper. It proposes an approach and reports no results.
Authors
Arnab Phani, Elias Strauss, Tim Schuster, Sebastian Schelter
Abstract
Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development. Using large language models for code generation and predictive performance as a machine-gradable feedback signal, these agents iteratively synthesize, execute, and refine pipeline code. Yet current systems are largely evaluated on pre-built, single-table benchmarks that abstract away key data management challenges. This tutorial introduces the foundations of MLE agents and highlights research opportunities for the database community. We cover how agents combine program synthesis with iterative and evolutionary optimization, and discuss open challenges in efficient pipeline execution, data discovery and augmentation over heterogeneous sources, reliability and debugging, as well as fairness and compliance. We connect these challenges to existing data management techniques and conclude with a hands-on session using contemporary MLE agents.