AutoML-GPT: Large Language Model for AutoML

TL;DR

AutoML-GPT leverages large language models to streamline AutoML processes, enabling users to efficiently build high-performing machine learning models through conversational interfaces and advanced optimization techniques.

Contribution

This paper introduces AutoML-GPT, a novel framework integrating LLMs with AutoML tools for automated, user-friendly machine learning pipeline management.

Findings

Reduces time and effort in ML tasks

Provides valuable insights and guidance during model training

Achieves optimal performance through advanced hyperparameter optimization

Abstract

With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users access to a wide range of data preprocessing techniques, feature engineering methods, and model selection algorithms. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. Throughout the process, AutoML-GPT employs advanced techniques for hyperparameter optimization and model selection, ensuring that the resulting model achieves optimal performance. The system effectively manages the complexity of the machine learning pipeline, guiding users towards the best choices without requiring deep domain knowledge. Through our experimental results on diverse datasets, we have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks. Its ability to leverage the vast knowledge encoded in large language models enables it to provide valuable insights, identify potential pitfalls, and suggest effective solutions to common challenges faced during model training.