AutoGluon, the most popular open-source library published by the Amazon Science team, is a state-of-the-art toolkit designed to make automated machine learning (AutoML) accessible and powerful across diverse data types, including tabular, text, image, and time series powered by foundational models. During this presentation, we introduce the latest advancements in AutoGluon, highlighting the Chronos model (3.4MM downloads in 1 month), foundation models for forecasting. Chronos significantly enhances the accuracy and efficiency of time series predictions with a 60% win-rate improvement over the previous version. AutoGluon 1.1 also brings major improvements to deep learning model automation, ease of use, and performance, particularly for large datasets. 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. Join us to explore how AutoGluon can solve real-world problems with just three lines of code and discover the cutting-edge techniques that make it a leading AutoML toolkit for researchers and practitioners.