Getting started

Amazon SageMaker is a fully managed, modular service that helps developers and data scientists to build, train, and deploy machine learning models at scale. Get started with these developer resources, so you can quickly move from concept to production.

Introduction to Amazon SageMaker

Learn about the build, train, and deploy modules of Amazon SageMaker.

Amazon SageMaker (1:03)

DEVELOPER GUIDE


Follow this step-by-step guide to start using Amazon SageMaker quickly.

TUTORIAL


Learn how to get started with Amazon SageMaker in 10 minutes.

Build machine learning models

Use the Build module of SageMaker to collect and prepare training data, access pre-built notebooks, and leverage the built-in, high performance algorithms.

DEVELOPER GUIDE


Learn to build an ML model with the steps and resources outlined in this guide.

DEVELOPER GUIDE


Follow this guide to start using notebook instances that are fully managed and pre-built with Amazon SageMaker.

BLOG


Read this blog and learn to use common workflows using Amazon SageMaker notebook instances.

HANDS-ON LAB


Acccess a rich repository of SageMaker notebooks, on GitHub.

HANDS-ON LAB


Utilize algorithms built into Amazon SageMaker that are faster and cheaper than popular alternatives.

WEBINAR


In this on-demand tech talk, learn to build intelligent applications using Amazon SageMaker and other AWS services.

Train and tune machine learning models

Use the Train module to set up training environments with one click and optimize your model using automatic module tuning

DEVELOPER GUIDE


Read an overview of how to train machine learning models using Amazon SageMaker.

DEVELOPER GUIDE


Learn how to incrementally train a machine learning model saving time and resources.

DEVELOPER GUIDE


Follow detailed steps to tune your model to the highest accuracy using automatic model tuning. You can find the best version of your model by automatically running many training jobs using your chosen algorithms and ranges of hyperparameters for those algorithms.

HANDS-ON LAB


Try these examples of using hyperparameter tuning across different algorithms and deep learning frameworks.

BLOG


Learn how to automatically tune the hyperparameter values of the algorithm in your machine learning model to obtain the most accurate predictions.

WEBINAR


In this on-demand tech talk, learn to train TensorFlow-based machine learning models. Understand the unique combination of TensorFlow and Amazon SageMaker to accelerate training of your machine learning models and bring them to production.

Deploy machine learning models

Use the Deploy module to deploy your machine learning models to production with a single click.

DEVELOPER GUIDE


Follow the step-by-step guide to deploy machine learning models on the highest performing infrastructure.

DEVELOPER GUIDE


Understand the best practices to deploy machine learning models at scale using Amazon SageMaker.

HANDS-ON LAB


Follow the examples on GitHub to use Amazon SageMaker and AWS Step Functions to automate the building, training, and deploying of custom machine learning models.

BLOG


Learn to use the deployment capabilities of SageMaker including A/B testing and Auto Scaling, delivering high performance and high availability for your machine learning models.

WEBINAR


In this on-demand tech talk, learn about the machine learning life cycle, best practices for using Amazon SageMaker in your enterprise, and how to integrate Amazon SageMaker with other AWS services.

Additional resources

SDKs

Use APIs tailored to your programming language or platform to make it easy to use Amazon SageMaker in your applications.

What's new

What’s New announcements are high-level summaries of launches and feature updates. Read Amazon SageMaker specific updates and other AWS announcements.

Read now »

No blog posts have been found at this time. Please see the AWS Blog for other resources. 

Learn more about Amazon SageMaker features

Visit the features pages
Have more questions?
Contact us