To achieve state-of-the-art performance in machine learning (ML), it is very important to choose the right algorithm and its hyperparameters for a given dataset. Since finding the correct settings needs a lot of time and expert knowledge, we developed AutoML tools that can be used out-of-the-box with minimal expertise in machine learning. In this session, I will present two stateof-the-art tools in this field: (i) auto-sklearn (www.automl.org/auto-sklearn/) for classical machine learning and (ii) AutoPyTorch (www.automl.org/autopytorch/) for deep learning.