  Automated Machine Learning (AutoML) is used more than ever before to support
users in determining efficient hyperparameters, neural architectures, or even
full machine learning pipelines. However, users tend to mistrust the
optimization process and its results due to a lack of transparency, making
manual tuning still widespread. We introduce DeepCAVE, an interactive framework
to analyze and monitor state-of-the-art optimization procedures for AutoML
easily and ad hoc. By aiming for full and accessible transparency, DeepCAVE
builds a bridge between users and AutoML and contributes to establishing trust.
Our framework's modular and easy-to-extend nature provides users with
automatically generated text, tables, and graphic visualizations. We show the
value of DeepCAVE in an exemplary use-case of outlier detection, in which our
framework makes it easy to identify problems, compare multiple runs and
interpret optimization processes. The package is freely available on GitHub
https://github.com/automl/DeepCAVE.
