Dataset-level Governance for Machine Learning Projects

Question

Pulumi · Accepted Answer

Managing and controlling access to datasets is crucial for machine learning projects, particularly when dealing with sensitive information or aiming to maintain the quality and consistency of the data used. Dataset-level governance involves specifying who can access and modify data, tracking how data is used over time, and ensuring compliance with regulations and standards.

Pulumi provides integration with various cloud providers, including AWS, Azure, and GCP, which means we can leverage their respective services for managing machine learning datasets with appropriate governance controls.

We'll walk through how to use Pulumi to create dataset-level governance for machine learning projects on AWS using Amazon SageMaker, which allows users to build, train, and deploy machine learning models and datasets. The service provides features such as data versioning, data sharing, and granular access control to datasets.

Here's a Python program using Pulumi to set up dataset-level governance. We'll be using the `aws.sagemaker.Domain` resource which allows us to create a domain for our machine learning environment, where we can manage user access to data:

```python
import pulumi
import pulumi_aws as aws

# Create a SageMaker domain which provides an environment to create and manage datasets with governance.
sagemaker_domain = aws.sagemaker.Domain("sagemakerDomain",
    auth_mode="IAM",  # This determines who is allowed to authenticate to this domain. We're using AWS IAM here.
    default_user_settings={
        "execution_role": pulumi_aws.iam.Role("sagemaker_execution_role").arn,  # Role that users in this domain will assume when they log in.
        "security_groups": [pulumi_aws.ec2.SecurityGroup("sagemaker_sg").id],  # List of security groups associated with the users in the domain.
        "sharing_settings": {  # Settings related to sharing and collaboration within this domain.
            "s3_kms_key_id": pulumi_aws.kms.Key("sagemaker_kms_key").arn,  # KMS key for encrypting data shared within the domain.
            "notebook_output_option": "Allowed",  # Specifies the output options for the user notebooks within the domain.
            "s3_output_path": "s3://my-sagemaker-bucket/output",  # S3 path where user notebook outputs are stored.
        }
    },
    vpc_id=pulumi_aws.ec2.Vpc("sagemaker_vpc").id,  # VPC where the domain is located.
    subnet_ids=[pulumi_aws.ec2.Subnet("sagemaker_subnet").id],  # Subnets within the VPC where the domain is located.
    domain_name="my-sagemaker-domain",  # Human-readable name for the domain.
)

# Provide the domain endpoint URL to the user.
pulumi.export("sagemaker_domain_url", sagemaker_domain.url)
```

This code establishes a managed environment with Amazon SageMaker for your machine learning projects. Let's break down the key pieces:

- `auth_mode`: Defines the authentication mode for the domain, which in this example is set to use AWS IAM (Identity and Access Management).
- `default_user_settings`: Encapsulates settings for the execution role, security groups, sharing and encryption settings associated with the domain's users.
- `execution_role`: This IAM role grants sufficient permissions for SageMaker services to access AWS resources on your behalf.
- `security_groups`: Security groups work as a virtual firewall for instances to control inbound and outbound traffic for SageMaker instances.
- `sharing_settings`: Settings that define how SageMaker resources within the domain should be shared.
- `vpc_id` and `subnet_ids`: Networking setup which ensures SageMaker is running within your organization's network.
- `domain_name`: A human-friendly identifier for the SageMaker domain.

To ensure proper governance, you would also need to define policies around who can access and change the `execution_role` and `security_groups`, as well as manage the encryption keys used for securing the datasets.

Remember, this is a simplified example and assumes that the resources such as roles, security groups, VPC, and subnets are already defined elsewhere in your Pulumi program. In a real-world scenario, you would also create and manage these resources using Pulumi. Additionally, you'd need to handle policies for data access and security, which might include detailed IAM policies and access logs.

Finally, once your domain is created, SageMaker provides a URL (exported at the end of the script) that domain users can access to use various SageMaker capabilities in a way that adheres to the governance policies you've established.