AutoGluon: Powerful and Easy-to-use AutoML for Text, Image, and Tabular Data
Channel: Apache MXNet
Date: 2021-02-08

Dr. Jonas Mueller, Applied Scientist @ AWS

AutoGluon is an open-source software toolkit to automate machine learning (ML) on image, text, and tabular data (built on top of MXNet).  With a couple lines of Python code, anybody can translate their raw data into highly accurate predictions, regardless of their ML expertise.  In comprehensive benchmarks (see https://arxiv.org/pdf/2003.06505.pdf) over 50 datasets, AutoGluon produced significantly more accurate models (given the same runtime/compute) than other popular AutoML tools like TPOT, H2O, AutoWEKA, auto-sklearn, Google AutoML Tables.  In two prominent Kaggle competitions with tabular data, AutoGluon outperformed 99% of human data science teams after just being run for 4 hours on the raw data.  In comparisons with human data scientists on 4 image classification competitions from Kaggle, AutoGluon consistently ranked around the top 10%.  AutoGluon has also been used to win a number of internal prediction competitions at Amazon, and is being used in production for multiple applications.  This talk describes how AutoGluon achieves such strong predictive performance through state-of-the-art deep learning, hyperparameter-optimization, stack ensembling, and other modelling techniques.