Deploy a Model in Studio
After you register a model version and approve it for deployment, deploy it to a Amazon SageMaker AI endpoint for real-time inference. You can Deploy a Model from the Registry with Python or deploy your model in Amazon SageMaker Studio. The following provides instructions on how to deploy your model in Studio.
This feature is not available in Amazon SageMaker Studio Classic.
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If Studio is your default experience, the UI is similar to the images found in Amazon SageMaker Studio UI overview.
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If Studio Classic is your default experience, the UI is similar to the images found in Amazon SageMaker Studio Classic UI Overview.
Before you can deploy a model package, the following requirements must be met for the model package:
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A valid inference specification available. See InferenceSpecification for more information.
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Model with approved status. See Update the Approval Status of a Model for more information.
The following provides instructions on how to deploy a model in Studio.
To deploy a model in Studio
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Open the Studio console by following the instructions in Launch Amazon SageMaker Studio.
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Choose Models from the left navigation pane.
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Choose the Registered models tab, if not selected already.
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Immediately below the Registered models tab label, choose Model Groups, if not selected already.
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(Optional) If you have models that are shared with you, you can choose between My models or Shared with me.
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Select the checkboxes for the registered models. If the above requirements are met, the Deploy button becomes available to choose.
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Choose Deploy to open the Deploy model to endpoint page.
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Configure the deployment resources in the Endpoint settings.
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Once you have verified the settings, choose Deploy. The model will then be deployed to the endpoint with the In service status.
