Amazon SageMaker

Supported labeling types: Row labeling.

You can integrate your Amazon SageMaker Inference Endpoint for data labeling with ML-assisted labeling.

Enable ML-assisted labeling extension

1. Create a custom project for row labeling.

2. Click the gear icon from the extension panel to open the Manage extensions dialog.

3. Enable the ML-assisted labeling extension.

Manage extensions dialog

1. Once enabled, select Amazon SageMaker as the service provider.

Setup role in AWS Identity and Access Management (IAM)

Before creating a role in the IAM page, create the required policies to define permissions for the new role.

1. Go to your IAM page, navigate to the Policies section, and then create a new policy.

2. Download the example below and add it.

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sagemaker-s3-specific-permissions.txt

Open

After that, proceed to create a new role:

1. Go to the IAM page and navigate to the Roles section.

2. Click Create Role, then select AWS account as the role type.

3. Enter the Account ID and External ID. These values are automatically generated in the Datasaur ML-assisted labeling extension and can be copied into AWS.

Select trusted entity

1. On the Add permissions step, search the policy you created before and add them to the permissions.

2. Provide a role name and click Create role. You can check the trusted policies config below. The role will be created successfully.

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trust-policies.txt

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1. View your role and copy the Role ARN.

1. Paste this information into the ML-assisted labeling extension.

2. Go back to the Amazon SageMaker page and retrieve the Endpoint ARN. Copy it and paste it into the extension.

Endpoint details

Start prediction

Once you have configured the settings, click Predict labels to generate predictions from your model.

Prediction appears