Multi-environment ML Model Configurations with Access Control

Question

Pulumi · Accepted Answer

To create multi-environment machine learning (ML) model configurations with access control, we will use Pulumi with the AWS provider. Setting this up involves creating different environments (e.g., development, staging, production) for the ML model deployment. This usually means provisioning different infrastructure environments for each stage, separated by AWS accounts or at least different AWS regions or VPCs. AWS SageMaker provides a way to build, train, and deploy machine learning models, and it supports various mechanisms for access control.

We will use AWS SageMaker to set up the ML model configurations and AWS Identity and Access Management (IAM) policies for access control. Pulumi allows us to define and deploy this infrastructure as code.

Here's what we are going to do:

1. Define a SageMaker Project to hold our ML models and configurations.
2. Define IAM roles and policies that control who can access the SageMaker environment and under what conditions.

The following program sets up a basic SageMaker project which can contain your ML models. The program will also create an IAM role with a policy that grants necessary permissions to access SageMaker services and resources. This role can then be attached to users or services that need to interact with the SageMaker project.

The IAM policy here is a broad-strokes example and should be refined to match the security requirements of your specific use case. Similarly, in a real-world scenario, you'd create multiple SageMaker projects and IAM roles, one for each environment, and scope the access accordingly.

```python
import pulumi
import pulumi_aws as aws

# Create an AWS SageMaker Project.
sagemaker_project = aws.sagemaker.Project("multi_env_ml_project",
    description="SageMaker Project for multi-environment ML model configurations",
    service_catalog_provisioning_details={
        "productId": "prod-abcd1234", # replace this with your actual product ID
    }
)

# Define an IAM role that will be used by SageMaker to access AWS resources.
sagemaker_role = aws.iam.Role("sagemaker_role",  
    assume_role_policy=pulumi.Output.all(sagemaker_project.name).apply(lambda name: f'''
    {{
      "Version": "2012-10-17",
      "Statement": [
        {{
          "Effect": "Allow",
          "Principal": {{
            "Service": "sagemaker.amazonaws.com"
          }},
          "Action": "sts:AssumeRole",
          "Condition": {{
              "StringEquals": {{
                  "sts:ExternalId": "{name}"
              }}
          }}
        }}
      ]
    }}
    ''')
)

# Define an IAM policy to control access to SageMaker resources.
sagemaker_policy = aws.iam.Policy("sagemaker_policy",
    policy=pulumi.Output.all(sagemaker_project.name).apply(lambda name: f'''
    {{
        "Version": "2012-10-17",
        "Statement": [
            {{
                "Effect": "Allow",
                "Action": [
                    "sagemaker:*"
                ],
                "Resource": "*"
            }}
        ]
    }}
    ''')
)

# Attach the IAM policy to the role.
role_policy_attachment = aws.iam.RolePolicyAttachment("sagemaker_role_policy_attachment",
    role=sagemaker_role.name,
    policy_arn=sagemaker_policy.arn,
)

# Export the roles and project names to be used in other Pulumi programs or referenced elsewhere.
pulumi.export("sagemaker_project_name", sagemaker_project.name)
pulumi.export("sagemaker_role_name", sagemaker_role.name)
```

The SageMaker project will act as a container for your ML environments and configurations. Its details, like the product ID for service catalog provisioning, need to be populated with relevant details pertaining to your AWS environment.

The IAM role and policies are crucial in defining who can do what within this environment. The policy and role created here essentially allow all actions (`sagemaker:*`) on SageMaker resources. In practice, you'll want to narrow these permissions to enforce the principle of least privilege.

The role's trust policy specifies that only SageMaker can assume this role. Note the `sts:ExternalId` condition, which ties the role assumption ability to the specific SageMaker project created earlier, enhancing security by preventing the "confused deputy" problem.

For even more granular access control, you can define specific permissions for each environment and manage them separately, creating individual roles and policies for development, staging, and production, each with minimum privileges needed for the tasks at hand.

Remember to replace placeholders like `"productId": "prod-abcd1234"` with actual values from your AWS setup.

By using Pulumi to set up your infrastructure, you can easily replicate this configuration across multiple AWS accounts or regions, as needed for different environments, by just adjusting the parameters and running the program again.