AutoGluon v1.0: Shattering the AutoML Ceiling
with Zero Lines of Code

Date: Tuesday, Sep 12, 2023, 16:30 - 18:30

  • Nick Erickson, Amazon
  • Oleksandr Shchur, Amazon
  • David Salinas, Amazon


AutoGluon is an open source AutoML framework that shattered the SOTA in AutoML on its initial release in 2019 and continues to push the boundaries of what AutoML can achieve. AutoGluon takes inspiration from competition winning solutions on sites like Kaggle and redefines AutoML by ensembling multiple models and stacking them in multiple layers. The first half of the tutorial will give an overview of AutoGluon followed by a deep dive into how (and why) it has proven to be so effective. A hands-on coding section will be included.

The second half will focus on three pillars of AutoML innovation being worked on for AutoGluon v1.0 (2023).

The first is a large-scale benchmarking framework capable of automatically evaluating new contributions to the system efficiently and at scale to determine how the overall system performance is impacted to enhance transparency of acceptance criteria and inclusiveness of community contributions.

The second is AutoGluon-Zeroshot, an ensemble-based zeroshot-HPO & meta-learning portfolio optimization suite, which automatically identifies an optimal portfolio of models across hundreds of datasets and multiple modalities efficiently via simulated ensembling.

The final pillar will discuss the usage of large language models to extend the application and strength of AutoML to new heights.


Nick Erickson

Nick Erickson is a Senior Applied Scientist at Amazon AI. He obtained his master’s degree in Computer Science and Engineering from the University of Minnesota Twin Cities. He is the co-author and lead developer of the open-source AutoML framework AutoGluon. Starting as a personal competition ML toolkit in 2018, Nick continually expanded the capabilities of AutoGluon and joined Amazon AI in 2019 to open-source the project and work full time on advancing the state-of-the-art in AutoML.

Nick has given invited talks and tutorials on AutoGluon at ICML AutoML Workshop 2020 (Keynote), KDD 2020, ICML AutoML Workshop 2021, KDD 2022, AutoML Conf 2022 (Keynote), AutoML Fall School 2022, NeurIPS 2022, & PyData Seattle 2023. He has published papers on the topic of AutoML at ICML (2 accepted in 2023), NeurIPS, and JMLR. The original AutoGluon-Tabular paper has received 321 citations as of May 26th 2023.

Oleksandr Shchur

Oleksandr Shchur is an Applied Scientist at Amazon Web Services, where he works on time series forecasting in AutoGluon. Before joining AWS, he completed a PhD in Machine Learning at the Technical University of Munich, Germany, doing research on probabilistic models for event data. His research interests include machine learning for temporal data and generative modeling. Oleksandr is the lead developer of AutoGluon-TimeSeries and has given an invited talk on AutoGluon at PyData Berlin 2023. He has published papers at ICLR, ICML, KDD, and NeurIPS.