Automated Machine Learning (AutoML) continues to revolutionize how machine learning models are developed, making it accessible to practitioners with varying levels of expertise. In this workshop, we present the latest advancements in AutoGluon 1.2, an open-source AutoML toolkit developed by Amazon, which empowers users to achieve state-of-the-art performance across diverse machine learning tasks with minimal coding effort. We will emphasize how foundational models can streamline and enhance AutoML performance. Specifically, we will discuss our TabPFN-Mix and Chronos foundational model families for tabular and time series data, respectively. In addition, 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 model, we emphasize on how you can take advantage of a diverse collection of models to build an automated ML pipeline.