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Building State-Of-The-Art Machine Learning Models With AutoGluon
AutoGluon and AutoML
AutoGluon is an open-source AutoML framework built by AWS, that enables easy to use and easy to extend AutoML. It enables you to achieve a state of art predictive accuracy by utilizing state of the art deep learning techniques without expertise. It is also a quick way to prototype what you can achieve from your dataset as well as get an initial baseline for your machine learning. AutoGluon currently supports working with tabular data, text prediction, image classification, and object detection.
AutoML frameworks exist to reduce the bar for getting started with machine learning. They take care of the heavy lifting tasks like data preprocessing, feature engineering, algorithm selection, and hyperparameter tuning. This means, given a dataset and a machine learning problem, keep training different models with different combinations of hyperparameters until you find the optimum combination of model and hyperparameters — also referred to as CASH (combined algorithm/hyperparameter tuning). Existing AutoML frameworks include SageMaker Autopilot, Auto-WEKA, and Auto-sklearn.
AutoGluon is different from other (traditional) AutoML frameworks it does more than CASH (combined algorithm/hyperparameter tuning).
