1. Granting Access to ML Services with IAM Roles for Service Accounts


    To grant access to Machine Learning (ML) services using IAM roles for service accounts, you can create a role that has policies granting the necessary permissions to the ML services. This role will then be assumed by a service account that your application or service uses.

    Below is a detailed explanation of how to use Pulumi to create an AWS IAM role with the necessary permissions to access AWS ML services, and then to associate that role with a service account.

    1. IAM Role: We'll create an IAM role that includes a trust policy allowing it to be assumed by service accounts.
    2. Managed Policy: Optionally, If your use case requires specific permissions (e.g., access to Amazon S3 buckets or SageMaker), you can attach AWS managed policies or your custom policies to this role.
    3. Service Account Association: Finally, we'll create a service account or use an existing one and attach the IAM role to it.

    The resources will be created using pulumi_aws which is the Pulumi SDK for AWS. Specifically, we'll use aws.iam.Role for creating the IAM role and aws.iam.RolePolicyAttachment for attaching a policy to the role.

    Let's write the Pulumi program in Python:

    import pulumi import pulumi_aws as aws # Create an IAM role for a service account with a trust relationship policy # that allows 'sts:AssumeRole' from the service account. ml_service_role = aws.iam.Role("mlServiceRole", assume_role_policy=pulumi.Output.all( # You would replace `SERVICE_ACCOUNT_ID` with the actual service account identifier service_account_id="SERVICE_ACCOUNT_ID" ).apply(lambda args: f''' {{ "Version": "2012-10-17", "Statement": [ {{ "Effect": "Allow", "Principal": {{ "Service": "{args[0]}.iam.amazonaws.com" }}, "Action": "sts:AssumeRole" }} ] }} ''') ) # Attach a managed policy to the role. For this example, we are attaching the 'AmazonSageMakerFullAccess' policy. # Allowing full access to SageMaker might not be recommended for production; you should follow the # principle of least privilege and only grant the necessary permissions. sagemaker_policy_attachment = aws.iam.RolePolicyAttachment("sagemakerPolicyAttachment", role=ml_service_role.name, policy_arn="arn:aws:iam::aws:policy/AmazonSageMakerFullAccess" ) # ... You can attach more policies according to your requirements ... # Export the role ARN to be used elsewhere (e.g., in your application code or other parts of your infrastructure). pulumi.export("mlServiceRoleArn", ml_service_role.arn)

    In the above program:

    • We define an IAM role mlServiceRole. The role's trust policy allows an IAM principal to assume the role. You will need to replace SERVICE_ACCOUNT_ID with the correct identifier for your setup.
    • We attach the AmazonSageMakerFullAccess managed policy to this role. This policy grants permission to use Amazon SageMaker services. Please note that it is best practice to grant only the permissions required for your use case. The full access policy is used here for demonstration purposes.
    • Finally, we export the Amazon Resource Name (ARN) of the role so that it can be referenced in other parts of our Pulumi code or within your application.

    With this setup, any service that assumes this IAM role will have permissions to access and perform actions with AWS SageMaker services, which might include training ML models, deploying them, and managing your ML workflows. You can customize this example to fit other AWS ML services by attaching the appropriate policies to the IAM role.