# Deploy a Model in Studio
<a name="model-registry-deploy-studio"></a>

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](model-registry-deploy.md) 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.
+ If Studio is your default experience, the UI is similar to the images found in [Amazon SageMaker Studio UI overview](studio-updated-ui.md).
+ If Studio Classic is your default experience, the UI is similar to the images found in [Amazon SageMaker Studio Classic UI Overview](studio-ui.md).

Before you can deploy a model package, the following requirements must be met for the model package:
+ A valid inference specification available. See [InferenceSpecification](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModelPackage.html#sagemaker-CreateModelPackage-request-InferenceSpecification) for more information.
+ Model with approved status. See [Update the Approval Status of a Model](model-registry-approve.md) for more information.

The following provides instructions on how to deploy a model in Studio.

**To deploy a model in Studio**

1. Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md).

1. Choose **Models** from the left navigation pane.

1. Choose the **Registered models** tab, if not selected already.

1. Immediately below the **Registered models** tab label, choose **Model Groups**, if not selected already.

1. (Optional) If you have models that are shared with you, you can choose between **My models** or **Shared with me**.

1. Select the checkboxes for the registered models. If the above requirements are met, the **Deploy** button becomes available to choose.

1. Choose **Deploy** to open the **Deploy model to endpoint** page.

1. Configure the deployment resources in the **Endpoint settings**. 

1. Once you have verified the settings, choose **Deploy**. The model will then be deployed to the endpoint with the **In service** status.

For `us-east-1`, `us-west-2`, `ap-northeast-1`, and `eu-west-1` regions, you can use the following instructions to deploy models:

**To deploy a model in Studio**

1. Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md).

1. Choose **Models** from the left navigation pane.

1. Choose the **My models** tab.

1. Choose the Logged **models** tab, if not selected already.

1. Select a model and choose **View Latest Version**.

1. Choose **Deploy** and select between SageMaker AI or Amazon Bedrock.

1. Once you have verified the settings, choose **Deploy**. The model will then be deployed to the endpoint with the **In service** status.