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. AutoGluon, an open-source AutoML framework, makes state-of-the-art AutoML accessible to everyone. With just 3 lines of code, AutoGluon enables users to train and deploy high-accuracy models for computer vision, natural language processing, time series forecasting, and tabular data tasks with multimodality support. Behind its ease of use, AutoGluon leverages techniques like fusion from foundation models and advanced stacking and ensembling to achieve industry-leading performance.
This tutorial will demonstrate how AutoGluon empowers users to build, optimize and deploy performant machine learning models. We will cover basic usage for quick prototyping as well as more advanced functionality for maximizing predictive accuracy for various ML tasks. We will also cover important MLOps topics such as automatic large scale training, deployment, and benchmarking using cloud infrastructure empowered by AutoGluon’s ecosystem. Finally, we will discuss how AutoGluon is leveraging large language models to create an interactive automated data science (AutoDS) assistant. Through hands-on exercises, attendees will experience how AutoGluon delivers the full promise of AutoML to their fingertips.