News

New SageMaker Autopilot Automates AWS Machine Learning Models

• By Becky Nagel
• 12/04/2019

Amazon has unveiled SageMaker Autopilot, a new tool for its SageMaker machine learning platform that the company says can boost machine learning projects by automating tasks such as data preprocessing, training parameters and classification.

Within SageMaker -- the company's managed service for machine learning -- companies can work within the Studio IDE environment to "stitch together" different tools, including:

• SageMaker Neo, a training tool
• SageMaker Augmented AI, for "human review" of model predictions
• SageMaker Model Tuning for automated optimization
• and, now, SageMaker Autopilot, for automating the process of building and training machine learning models

As for how it works, Amazon shared the following:

SageMaker Autopilot first inspects your data set, and runs a number of candidates to figure out the optimal combination of data preprocessing steps, machine learning algorithms and hyperparameters. Then, it uses this combination to train an Inference Pipeline, which you can easily deploy either on a real-time endpoint or for batch processing. As usual with Amazon SageMaker, all of this takes place on fully-managed infrastructure.

Last but not least, SageMaker Autopilot also generate Python code showing you exactly how data was preprocessed: not only can you understand what SageMaker Autopilot did, you can also reuse that code for further manual tuning if you're so inclined.

Amazon is emphasizing that SageMaker Autopilot allows inspection of what's happening underneath, unlike with a "black box" model or other, more obtuse, tools.

A detailed tutorial on how to get started with the Autopilot tool can be found further down the page here.

SageMaker Autopilot is now live and available via an Amazon SageMaker subscription, which can be found here.