Automated machine learning (AutoML) offers the promise of translating raw data into accurate predictions without the need for significant human effort, expertise, and manual experimentation. In this workshop, we introduce AutoGluon, a state-of-the-art and easy-to-use toolkit that empowers multimodal AutoML. Different from most AutoML systems that focus on solving tabular tasks containing categorical and numerical features, we consider supervised learning tasks on various types of data including tabular features, text, image, time series, as well as their combinations. We will introduce the real-world problems that AutoGluon can help you solve within three lines of code and the fundamental techniques adopted in the toolkit. Rather than diving deep into the mechanisms underlining each individual ML models, we emphasize on how you can take advantage of a diverse collection of models to build an automated ML pipeline. Our workshop will also emphasize on the techniques behind automatically building and training deep learning models, which are powerful yet cumbersome to manage manually.

Check workshop website: https://autogluon.github.io/neurips2022-autogluon-workshop/