Prepare data for ML in minutes

Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow from a single visual interface. In addition, SageMaker Data Wrangler provides a unified experience between data preparation and training a ML model in Amazon SageMaker Autopilot. With just a few clicks, you can automatically build, train, and tune ML models, making it easier to complete feature engineering, and train, tune, and deploy high-quality ML models.

Amazon SageMaker Data Wrangler

Automatically create machine learning models

Amazon SageMaker Autopilot

With Amazon SageMaker Autopilot, you can automatically build, train, and tune the best ML models based on your data, while maintaining full control and visibility. SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and 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 models in Amazon SageMaker Studio to further improve the model quality.

Amazon SageMaker Autopilot

Use built-in algorithms and pre-built solutions to accelerate your ML journey

Amazon SageMaker JumpStart

Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. SageMaker JumpStart provides access to hundreds of built-in algorithms with pre-trained models from popular model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV, through the user interface. SageMaker JumpStart also provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your machine learning journey.

Amazon SageMaker JumpStart

Customer success


"Amazon SageMaker Data Wrangler enables us to hit the ground running to address our data preparation needs with a rich collection of transformation tools that accelerate the process of machine learning data preparation needed to take new products to market. In turn, our clients benefit from the rate at which we scale deployed models enabling us to deliver measurable, sustainable results that meet the needs of our clients in a matter of days rather than months.”

Frank Farrall, Principal, AI Ecosystems and Platforms Leader, Deloitte


"With the help of Amazon SageMaker Autopilot, our customers can automatically generate the best ML models based on unique datasets. Thanks to SageMaker Autopilot, we can provide personalized insights to tens of millions of shoppers tapping into the power of AutoML."

Alexander Jost, CEO, RetentionX


“Thanks to Amazon SageMaker JumpStart, we can have better starting points which makes it so that we can deploy a ML solution for our own use cases in 4-6 weeks instead of 3-4 months.”

Gus Nguyen, Software Engineer, MyCase



Prepare training data for machine learning with minimal code using Amazon SageMaker Data Wrangler


Automatically create a machine learning model with Amazon SageMaker Autopilot


Get started with your machine learning project quickly using Amazon SageMaker JumpStart


SageMaker Fridays: Low-code machine learning


Experiment faster with low-code/no-code machine learning tools


Develop and deploy ML models

Get started with a tutorial

Follow the step-by-step tutorial to learn how to deploy a model using Amazon SageMaker.

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Amazon Pinpoint getting started tutorial
Try a self-paced workshop

In this hands-on lab, learn how to use Amazon SageMaker to build, train, and deploy an ML model.

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Start building in the console

Get started building with Amazon SageMaker in the AWS Management Console.

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