1. Inter-region AI Data Sharing Using Spaces and CORS


    To facilitate inter-region AI data sharing using cloud services, several steps and resources would typically be involved. Spaces in cloud services can refer to workspaces or environments dedicated to specific tasks like machine learning, data analysis, or team collaboration. CORS (Cross-Origin Resource Sharing) is a security feature that allows or restricts resources to be requested from another domain outside the domain from which the resource originated. However, CORS is more of a web application concept and not commonly associated with data sharing on cloud AI services.

    For an AI-focused setup, you might want to leverage services like Amazon SageMaker, AWS Clean Rooms, or Azure Machine Learning. These platforms support the sharing and collaboration of AI models and datasets. Considering Pulumi's Infrastructure as Code approach, we can write a program that creates and configures an Amazon SageMaker Space for this purpose. AWS SageMaker Spaces can be useful for team members to share notebooks and datasets.

    Here's a Pulumi program written in Python to create an Amazon SageMaker Space:

    import pulumi import pulumi_aws as aws # Create an Amazon SageMaker domain that the Space will belong to. # A domain represents a shared space in SageMaker where users can collaboratively work on machine learning projects. sagemaker_domain = aws.sagemaker.Domain("mySagemakerDomain", auth_mode="IAM", default_user_settings=aws.sagemaker.DomainDefaultUserSettingsArgs( execution_role="arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001", security_groups=["sg-12345678"], sharing_settings=aws.sagemaker.DomainSharingSettingsArgs( notebook_output_option="Allowed", s3_kms_key_id="alias/sagemaker-kms-key", s3_output_path="s3://my-sagemaker-bucket/output", ), ), domain_name="my-domain", subnet_ids=["subnet-12345678"], vpc_id="vpc-12345678", ) # Create an Amazon SageMaker Space within the domain. # A Space in SageMaker is a purpose-built environment for machine learning that enables you to build, train, and deploy models. sagemaker_space = aws.sagemaker.Space("mySagemakerSpace", domain_id=sagemaker_domain.id, space_name="my-space", space_settings=aws.sagemaker.SpaceSpaceSettingsArgs( jupyter_server_app_settings=aws.sagemaker.SpaceJupyterServerAppSettingsArgs( default_resource_spec=aws.sagemaker.SpaceResourceSpecArgs( instance_type="system", sagemaker_image_arn="arn:aws:sagemaker:us-west-2:123456789012:image/sagemaker-custom-image", sagemaker_image_version_arn="arn:aws:sagemaker:us-west-2:123456789012:image-version/sagemaker-custom-image/1.0", ), ), ), ) # Export the URL of the Jupyter server endpoint within the SageMaker Space. pulumi.export('domain_url', sagemaker_domain.url) pulumi.export('sagemaker_space_name', sagemaker_space.space_name)

    In this Python program, we perform the following:

    1. Import the necessary modules from the Pulumi AWS SDK.
    2. Create a SageMaker domain, which provides a shared space for collaborative machine learning development.
      • The auth_mode specifies the authentication mode, IAM in this case.
      • The default_user_settings configures the execution role, security groups, sharing settings for notebook output, KMS key for encryption, and the S3 output path for results.
    3. Create a SageMaker Space within the domain for a more focused workspace dedicated to a task or team.
      • The domain_id links the space to the created domain.
      • The space_name gives a name to the space.
      • The space_settings define details about the Jupyter server application used in the space, including resource specifications like instance type and SageMaker image ARNs.
    4. Export the URL of the domain and the name of the SageMaker Space so these can be accessed as outputs after deploying the Pulumi stack.

    This is a simple representation of how you can set up a collaborative environment for your AI projects using AWS and Pulumi. Keep in mind that the actual implementation might require more configurations based on specific use cases, security considerations, and further integrations with data storage and processing services. Additionally, SageMaker supports direct control over CORS settings for its endpoints which you would have to configure as per your requirements for cross-origin resource sharing. This is just one aspect of the complex process of sharing AI data, and you might need additional resources like databases, data lakes, or API gateways depending on your architecture.