Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning models based on your data, while allowing you to maintain full control and visibility.

Building machine learning (ML) models requires you to manually prepare features, test multiple algorithms, and optimize hundreds of model parameters in order to find the best model for your data. However, this approach requires deep ML expertise. If you don’t have that expertise, you could use an automated approach (AutoML), but AutoML approaches typically provide very little visibility into the impact of your features for model predictions. As a result, you may have less trust in it because you can’t recreate it and you can’t learn how it makes predictions.

Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.