MLE-STAR: A state-of-the-art machine learning engineering agent
We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.
MLE-STAR is a state-of-the-art machine learning engineering agent capable of automating various machine learning tasks across diverse data modalities while achieving top performances.
The rise of machine learning (ML) has fueled the development of high-performance applications across a wide array of real-world scenarios, from tabular classification to image denoising. However, crafting these models remains an arduous endeavor for machine learning engineers, demanding extensive iterative experimentation and data engineering. To streamline these demanding workflows, recent investigations have concentrated on leveraging large language models (LLMs) as machine learning engineering (MLE) agents. By capitalizing on their inherent coding and reasoning skills, these agents conceptualize ML tasks as code optimization challenges. They then explore potential code solutions, ultimately generating executable code (such as a Python script) based on a provided task description and datasets.
ML engineering agents are built to tackle diverse machine learning challenges by analyzing a task description and datasets that can span various modalities. Their ultimate goal is to pinpoint the best solution for the given problem.
