SageMaker JumpStart - Amazon SageMaker
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SageMaker JumpStart

SageMaker JumpStart provides pre-trained, open-source models for a wide range of problem types to help you get started with machine learning. You can incrementally train and tune these models before deployment. JumpStart also provides solution-templates that set up infrastructure for common use cases, and executable example notebooks for machine learning with SageMaker.

You can access the pre-trained models, solution templates, and examples through the JumpStart landing page in Amazon SageMaker Studio. The following steps show how to access JumpStart models and solutions using Amazon SageMaker Studio.

You can also access the models using the SageMaker Python SDK. For information about how to use JumpStart models programmatically via API, see Use SageMaker JumpStart Algorithms with Pretrained Models.

Open and use JumpStart

The following sections give information on how to open and use JumpStart from the Amazon SageMaker Studio UI.

Open JumpStart

In Amazon SageMaker Studio, open JumpStart by using one of the following:

  • The JumpStart launcher in the Get Started section.

  • The JumpStart icon ( 
              The JumpStart icon.
            ) in the left sidebar.

  • The Browse JumpStart button in the launched assets pane.

          SageMaker Studio interface with JumpStart Launcher, JumpStart icon, and Browse
            JumpStart button.

Before downloading or using third-party content: You are responsible for reviewing and complying with any applicable license terms and making sure that they are acceptable for your use case.

Use JumpStart

From the SageMaker JumpStart landing page, you can browse for solutions, models, notebooks, and other resources. You can also view your currently launched solutions, endpoints, and training jobs. Using the JumpStart search bar, you can search for topics of interest.

            The SageMaker JumpStart landing page with search bar and autosuggest options.

You can find JumpStart resources by using search, or by browsing each category that follows the search panel:

  • Featured – The latest or most used solutions, models, and examples.

  • Solutions – In one step, launch comprehensive machine learning solutions that tie SageMaker to other Amazon services. Select Explore All Solutions to view all available solutions.

  • Models – Find a model that fits your needs from the collection of text, vision, and tabular models. You can filter the collection by problem types, data types, and frameworks. Then, deploy and refine pre-trained models for image classification and object detection in one step. Select Explore All Models to view all available models.

  • Resources – Use example notebooks, blogs, and video tutorials to learn and head start your problem types.

    • Example notebooks – Run example notebooks that use SageMaker features like Spot Instance training and experiments over a large variety of model types and use cases.

    • Blogs – Read details and solutions from machine learning experts.

    • Video tutorials – Watch video tutorials for SageMaker features and machine learning use cases from machine learning experts.