AutoGluon: AutoML for Tabular, Multimodal and Time Series Data
Caner Turkmen, Oleksandr Shchur
AutoML, or automated machine learning, offers the promise of transforming raw data into accurate predictions with minimal human intervention, expertise, and manual experimentation. In this talk, we will introduce AutoGluon, a cutting-edge toolkit that enables AutoML for tabular, multimodal and time series data. AutoGluon emphasizes usability, enabling a wide variety of tasks from regression to time series forecasting and image classification through a unified and intuitive API. We will specifically focus on tasks on tabular and time series tasks where AutoGluon is the current state-of-the-art, and demonstrate how AutoGluon can be used to achieve competitive performance on tabular and time series competition data sets. We will also discuss the techniques used to automatically build and train these models, peeking under the hood of AutoGluon.
Affiliation: Amazon Web Services
Caner Turkmen is a Senior Applied Scientist at Amazon Web Services, where he works on problems at the intersection of machine learning and forecasting, in addition to developing AutoGluon-TimeSeries. Before joining AWS, he worked in the management consulting industry as a data scientist, serving the financial services and telecommunications industries on projects across the globe. Caner’s personal research interests span a range of topics, including forecasting, causal inference, and AutoML.
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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