As the demand for machine learning applications surges, it becomes evident that the available pool of knowledgeable data scientists cannot scale proportionally with the increasing data volumes and diverse application requirements in our digital world. To address this challenge, various automated machine learning (AutoML) frameworks have emerged, aiming to bridge the gap in human expertise by automating the construction of machine learning pipelines. AutoML research aims to automate the machine learning process progressively, with the objective of making effective methods accessible to everyone.

Therefore, the workshop is designed for a diverse audience, including core machine learning researchers involved in various ML domains related to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and explainability within the AutoML context. It also caters to domain experts seeking to apply machine learning to novel problem domains.