AutoML Framework

AutoML frameworks automate the process of building and optimizing machine learning models, aiming to reduce the need for expert intervention and accelerate the development cycle. Current research emphasizes extending AutoML to handle multimodal data, imbalanced datasets, and online learning scenarios, often leveraging techniques like Bayesian optimization, ensemble methods, and large language models for improved performance and interpretability. These advancements are significant because they broaden the accessibility of machine learning to non-experts and improve efficiency across diverse applications, from cybersecurity and recommender systems to financial modeling and drought prediction.