Our Mission
… is to make the world of Automated Machine Learning (AutoML) accessible and welcoming to everyone, whether you’re just beginning to explore its potential or are an experienced practitioner looking to stay up-to-date. This website is a one-stop hub for all things AutoML: from foundational resources and guides to the latest research, tools, and breakthroughs in the field.
We aim to foster a vibrant, collaborative community where enthusiasts, researchers, and industry professionals can connect, share insights, and support one another in leveraging AutoML effectively. Here, you’ll find up-to-date materials, discover new advancements, and engage in discussions that drive the future of AutoML forward.
Join us in building a connected, knowledgeable, and forward-thinking AutoML community!
Founding Members
AutoML-Space was founded by several academic partners
LUH|AI
Leibniz University Hannover
Prof. Dr. Marius Lindauer leads the AutoML group at the Leibniz University Hannover. His team was one of the driving factors of the first version of AutoML-Space.
AutoML for Science
University Tübingen
Dr. Katharina Eggensperger leads the “AutoML for Science” group as part of Tübing’s Excellence Cluster.
Statistical Learning & Data Science
Ludwig-Maximilians University Munich
Prof. Dr. Bernd Bischl and Prof. Dr. Matthias Feurer lead the chair of Statistical Learning & Data Science at the department of statistics.
Machine Learning Lab
Albert-Ludwigs University Freiburg
Prof. Dr. Frank Hutter leads the Machine Learning Lab at the University of Freiburg.
We acknowledge the core team working on the very first versions of this website (alphabetically sorted): Carolin Benjamins, Difan Deng, Theresa Eimer, Lukas Fehring, Leona Hennig, Marius Lindauer, Aditya Mohan, Tim Ruhkopf, Daphne Theodorakopoulos and Marcel Wever.
Supporters
AutoML-Space is supported in its mission and by contributed content
by teams at several organizations and companies.
AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to its define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.
If you would also like to contribute to automl.space, reach out to us.