Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
AutoGluon-Tabular
An AutoML algorithm that trains a multi-layer stack ensemble model to predict on regression/classification datasets directly from CSV data.
Product Overview
AutoGluon-Tabular can save time by automating time-consuming manual steps—handling missing data, manual feature transformations, data splitting, model selection, algorithm selection, hyperparameter selection and tuning, ensembling multiple models, and repeating the process when data changes. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers.
Key Data
Type
Algorithm
Highlights
AutoGluon-Tabular allows you to easily produce accurate models to predict the values in one column of a data table based on the rest of the columns' values. It is suitable for regression/classification tasks with tabular data containing text, categorical, and numeric features. Accuracy is automatically boosted via multi-layer stack ensembling, deep learning, and data-splitting (bagging) to curb overfitting.
Accurate: In benchmarks from the AutoGluon-Tabular paper, AutoGluon outperformed many popular open-source/commercial AutoML platforms on 50 classification/regression datasets from Kaggle/OpenML. AutoGluon is faster, more robust, and much more accurate than other tools, even outperforming the best-of five other AutoML platforms on most datasets. In two popular Kaggle competitions, AutoGluon beat 99% of the participating data scientists after just 4 hours of training.
Easy-to-use: AutoGluon requires no manual data preprocessing as long as your data live in a valid CSV table which can be loaded into Python via standard libraries like pandas. You do not need to specify the datatype for each feature. It works well out-of-the-box when some columns in your data contain text.
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Usage Information
Training
Training Input data
Supported MIME Types: text/csv
AutoGluon-Tabular requires no manual data preprocessing as long as your data is a valid CSV table. The first line of your CSV file should contain names for each column. Your data must contain "class" column which is a target column by default.
Example input(s):
Adult Data Set from UCI Dua, D. and Graff, C. (2019). UCI Machine Learning Repository . Irvine, CA: University of California, School of Information and Computer Science.
Model input and output details
Input
Summary
Inference Input data
At inference-time, you must provide test data (in CSV) that contains columns in the same order as the training dataset. However, the target variable is not required.
Supported Content Types: text/csv
Example input(s) for Real-time/Batch Inference:
Please refer to the sample notebook (https://github.com/awslabs/amazon-sagemaker-examples/tree/master/aws_marketplace/using_algorithms/autogluon )
Input MIME type
text/csvSample input data
'25, Private,178478, Bachelors,13, Never-married, Tech-support, Own-child, White, Female,0,0,40, United-States, <=50K\n'
Output
Summary
Output data MIME Content Type: application/json For both real-time Endpoint and Batch Transform Job, the outputs are the same.
Example output(s): Please refer to the sample notebook (https://github.com/awslabs/amazon-sagemaker-examples/tree/master/aws_marketplace/using_algorithms/autogluon )
Output MIME type
application/jsonSample output data
'{"class":{"0":"no"},"no":{"0":0.7825752497},"yes":{"0":0.2174247205}}'
Sample notebook
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Support Information
AutoGluon-Tabular
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