AutoML is a tool that aims to make machine learning more accessible by providing methods and processes to improve efficiency and accelerate research and application development in AI. Developed by academic research groups at the University of Freiburg, the Leibniz University of Hannover, and the University of Tübingen, AutoML focuses on automating various tasks in machine learning, such as hyperparameter optimization, neural architecture search, and dynamic algorithm configuration. It addresses the growing demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. With a collaborative effort among the research groups, AutoML offers state-of-the-art approaches and open-source tools to advance the field of AutoML. The tool has received significant funding and support from various sources, including ERC grants and collaborations with companies like Bosch.