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Create a VPC Endpoint - Amazon SageMaker AI
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Create a VPC Endpoint

You can create an interface endpoint to connect to SageMaker AI MLflow. For instructions, see Creating an interface endpoint. Make sure that you create interface endpoints for all of the subnets in your VPC from which you want to connect to SageMaker AI MLflow.

When you create an interface endpoint, ensure that the security groups on your endpoint allow inbound and outbound access for HTTPS traffic. For more information, see Control access to services with VPC endpoints.

Note

In addition to creating an interface endpoint to connect to SageMaker AI MLflow, create an interface endpoint to connect to the Amazon SageMaker API. When users call CreatePresignedMlflowTrackingServerUrl (for an MLflow tracking server) or CreatePresignedMlflowAppUrl (for an MLflow App) to get the URL to connect to SageMaker AI MLflow, the call goes through the interface endpoint. This endpoint is the one used to connect to the SageMaker API.

When you create the interface endpoint, specify the service name that corresponds to your MLflow offering:

  • For an MLflow tracking server, specify aws.sagemaker.Amazon Web Services Region.experiments as the service name.

  • For an MLflow App, specify aws.sagemaker.Amazon Web Services Region.mlflow as the service name.

After you create the interface endpoint, enable private DNS for your endpoint. When you connect to SageMaker AI MLflow from within the VPC using the SageMaker Python SDK, you connect through the interface endpoint instead of the public internet.

Within the Amazon Web Services Management Console, you can use the following procedure to create an endpoint.

To create an endpoint

  1. Navigate to the Amazon Virtual Private Cloud console.

  2. Navigate to Endpoints.

  3. Choose Create endpoint.

  4. (Optional) For Name (tag), specify a name for the endpoint.

  5. In the search bar under Services, specify experiments (for an MLflow tracking server) or mlflow (for an MLflow App).

  6. Select the endpoint that you're creating.

  7. For VPC, specify the name of the VPC.

  8. Choose Create endpoint.