1. Group-based Access Management for AI Training Platforms


    To implement group-based access management for AI training platforms with Pulumi, we would typically start by choosing a cloud provider and set up the necessary resources that the AI training platform requires. Depending on the cloud provider, these might include compute resources such as virtual machines or containers, storage resources to save training data and models, and networking resources to secure and enable communication between the services.

    To enforce group-based access management, we could use identity and access management (IAM) services provided by the cloud provider, which allow us to create user groups and assign policies that restrict access to certain resources or operations. For example, we might create a user group for data scientists and give them permissions to start training jobs and access data, but restrict their ability to modify the underlying infrastructure.

    Suppose we are using AWS as our cloud provider. In this case, we can use Pulumi to orchestrate resources such as Amazon SageMaker for training models, AWS Identity and Access Management (IAM) to manage access, and Amazon S3 for storing training data and models.

    Here's a basic Pulumi Python program that sets up an AWS SageMaker project, along with an IAM role and a user group for group-based access management:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store AI training data and models training_data_bucket = aws.s3.Bucket("training-data") # Create an IAM role for SageMaker to access resources like S3 buckets sagemaker_role = aws.iam.Role("sagemaker-role", assume_role_policy=aws.iam.get_policy_document(statements=[ aws.iam.GetPolicyDocumentStatementArgs( actions=["sts:AssumeRole"], principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs( type="Service", identifiers=["sagemaker.amazonaws.com"], )], ), ]).json ) # Create an IAM policy to give SageMaker access to the S3 bucket sagemaker_policy = aws.iam.Policy("sagemaker-policy", policy=training_data_bucket.arn.apply(lambda arn: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": [ "s3:GetObject", "s3:PutObject", "s3:DeleteObject" ], "Resource": f"{arn}/*" }] })) ) # Attach the IAM policy to the SageMaker role sagemaker_role_policy_attachment = aws.iam.RolePolicyAttachment("sagemaker-role-policy-attachment", role=sagemaker_role.name, policy_arn=sagemaker_policy.arn ) # Create a SageMaker project sagemaker_project = aws.sagemaker.Project("ai-training-project", service_catalog_provisioning_details={ "productId": "prod-abcdef", # Put appropriate productId here }, role_arn=sagemaker_role.arn, ) # Create an IAM group for data scientists data_scientists_group = aws.iam.Group("data-scientists") # Create an IAM policy that allows starting and stopping SageMaker training jobs data_scientist_policy = aws.iam.Policy("data-scientist-policy", policy=json.dumps({ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "sagemaker:CreateTrainingJob", "sagemaker:StopTrainingJob", # Add any additional SageMaker actions needed for data scientists ], "Resource": "*" # You may want to scope this down to specific resources } ] }) ) # Attach the data scientist policy to the data scientists group data_scientist_policy_attachment = aws.iam.GroupPolicyAttachment("data-scientist-policy-attachment", group=data_scientists_group.name, policy_arn=data_scientist_policy.arn ) # Export the S3 bucket name and SageMaker project ARN as stack outputs pulumi.export("training_data_bucket_name", training_data_bucket.bucket) pulumi.export("sagemaker_project_arn", sagemaker_project.arn)

    In this program, we are doing the following:

    • Creating an Amazon S3 bucket to store the AI training data and models.
    • Setting up an IAM role for AWS SageMaker with a trust policy that allows SageMaker to assume the role.
    • Attaching an IAM policy to the SageMaker role that grants necessary permissions to the training data S3 bucket.
    • Creating an AWS SageMaker project that uses the created IAM role.
    • Creating an IAM group data-scientists for our data scientists and attach a policy to it which allows members of this group to create and stop SageMaker training jobs.

    The productId for the service_catalog_provisioning_details in aws.sagemaker.Project is a placeholder and should be replaced with the actual product ID you intend to use within AWS's service catalog.

    Please make sure to replace any placeholders with actual resource IDs or the appropriate configurations needed for your specific setup. This program assumes you've previously authenticated with AWS and set up your Pulumi environment.