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Connect a SageMaker Model

To consume your predictions in Salesforce, connect your SageMaker model with Data 360 and define the prediction criteria for your use case.

Required Editions

Available in: All Editions supported by Data 360. See Data 360 edition availability.
USER PERMISSIONS NEEDED
Allow users to manage models in AI Models Enables you to create, update, and delete models in AI Models.
PERMISSION SETS
Data Cloud Architect Admin-level access to all AI Models features, including the ability to create, update, delete, and activate models.
Data Cloud User Restricted access to use a model, including getting predictions and improvements derived from a model.
  1. In AI Models (formerly Einstein Studio), on the Predictive tab, click Add Predictive Model.
    Model Builder view with the Predictive tab.
  2. To choose your model type, click Connect an Amazon SageMaker model. Then click Next.
    Model Builder with a view of the SageMaker model type.
  3. To connect the endpoint and access your model inferences, specify these details. Then click Next.
    Model Builder page that enables you to define various SageMaker endpoint parameters.
    • Name—Endpoint name
    • URL—Endpoint URL
    • Authentication—Key based or JWT (API gateway required for both)
    • Audience—Alphanumeric string or combination for JWT authentication.
    • Auth Header—Authorization header
    • Secret Key—Alphanumeric string or combination.
    • Request Format—Format for requesting data from the application or server.
      • JSON dense
      • CSV
    • Response Format—Format for providing data from a server response.
      • JSON
      • CSV
  4. To add schema, click Enter the inputs and outputs.
    Model Builder view that displays your model schema. You add your inputs and outputs here.
    1. To add inputs (variables) that can impact your prediction, click Add Input. Then specify a name, an API name, and select the data type. Save your work as you add the inputs.
    2. To add your outputs, click Add Output. Then specify a name, an API name, a data type, and the JSON Key in a format similar to “$.predictions”. Save your work as you add the outputs.
      Important
      Important You must position the inputs in the order that matches your SageMaker Select Query. To move an input, drag it to the preferred position in the Inputs section.
  5. Click Next.
  6. Review your model settings. Then save your work.
  7. To update your settings, click the section where you want to make changes. Then navigate through the flow to review all your settings.
  8. If you're satisfied, click Save.
  9. Name your model. Then click Save & Connect. After the model is connected, you can find it on the models list view, Predictive Models tab.
  10. To view your model details, click the model. You can activate, edit, or delete a model on this page.
  11. After the model is activated, go to the Usage tab. Here you can create prediction jobs that enable you to consume predictions from the model.
  12. If you created a batch prediction, click Run to refresh your prediction scores.
    Note
    Note With streaming predictions, new inferences are initiated only when there’s a change in the input on your Data Model Object (DMO).

See Also