Automated Machine Learning in the Era of Generative AI: A Comprehensive State-of-the-Art Review

Source abstract

This paper provides a comprehensive review of the state-of-the-art in Automated Machine Learning (AutoML) and Symbolic Regression (SR), focusing on the integration of Large Language Models (LLMs) and intelligent agents. We systematically analyze classical AutoML frameworks, End-to-End multi-agent systems, data-centric AI approaches, and hybrid methods combining LLMs with Bayesian Optimization. Each approach is evaluated based on its autonomy, computational efficiency, reliability, and suitability for highly regulated domains. The study highlights that while LLM agents offer high flexibility, they suffer from hallucinations and deployment complexities. Conversely, data-centric and hybrid methods provide better reliability and mathematical convergence. We conclude that no single universal solution exists and propose a pragmatic hybrid modular pipeline: utilizing data-centric agents for semantic data preparation, followed by robust classical or hybrid optimizers for mathematically guaranteed model selection.