Online autoML

Online AutoML aims to automate the entire machine learning pipeline, from data preprocessing to model deployment, thereby reducing the need for expert knowledge and accelerating the development of AI solutions. Current research emphasizes improving efficiency and usability through techniques like multi-agent frameworks leveraging large language models (LLMs) for task management and hyperparameter optimization, as well as developing specialized AutoML tools for specific tasks such as clustering and time series forecasting. This field is significant because it democratizes access to machine learning, enabling non-experts to build effective models and improving the efficiency of data scientists across various domains, including insurance, computer vision, and even legal applications.