1. Centralized Cross-Account AI Model Deployment


    When you need to deploy an AI model across multiple accounts in a centralized manner, it's essential to use a combination of cloud services that can manage resource sharing, permissions, and deployments effectively. Each major cloud provider offers different services to tackle these challenges.

    For example, if you're using AWS, you can leverage services like AWS IAM (Identity and Access Management) for managing permissions across accounts, AWS SageMaker for AI model training and deployment, and AWS Lambda for automation tasks. You could set up a centralized account that holds the model and then use IAM roles and SageMaker features to distribute the model to different accounts.

    Here's a basic breakdown of how you could architect this solution on AWS using Pulumi:

    1. IAM Roles: Create IAM roles in the central account with permissions that allow access to the SageMaker model.
    2. SageMaker Model: Train and deploy your model using SageMaker in the centralized account.
    3. Cross-Account Access: Use the IAM roles to give cross-account permissions to access the SageMaker model.
    4. Lambda Functions: Write Lambda functions, triggered by S3 events or CloudWatch events, that can manage the deployment of the model to various endpoints in different accounts.

    For the purposes of the Pulumi script, we'll focus on creating a SageMaker model and setting up cross-account permissions using IAM roles and policies. Let's get started with a Pulumi program that will outline this architecture.

    import pulumi import pulumi_aws as aws # Create a SageMaker role with the necessary permissions sagemaker_role = aws.iam.Role("sagemaker_role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole" } ] }""" ) # Attach policies to the SageMaker role that allow for model training and deployment aws.iam.RolePolicyAttachment("sagemaker_policy_attachment", role=sagemaker_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGE_MAKER_FULL_ACCESS ) # Deploy a SageMaker model resource sagemaker_model = aws.sagemaker.Model("aiModel", execution_role_arn=sagemaker_role.arn, primary_container={ "image": "174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1", "modelDataUrl": "s3://my-bucket/my-model/model.tar.gz" } ) # Create a policy that allows cross-account access to the SageMaker model cross_account_policy_document = aws.iam.get_policy_document( statements=[ { "principals": [ { "type": "AWS", "identifiers": ["arn:aws:iam::<Account B ID>:root"] } ], "actions": ["sagemaker:InvokeEndpoint"], "resources": [sagemaker_model.arn] } ] ) # Define a cross-account policy and attach it to the SageMaker role cross_account_policy = aws.iam.RolePolicy("cross_account_policy", role=sagemaker_role.name, policy=cross_account_policy_document.json, ) # Export the role ARN and SageMaker model ARN for cross-account access pulumi.export("sagemaker_role_arn", sagemaker_role.arn) pulumi.export("sagemaker_model_arn", sagemaker_model.arn)

    In the above program, we:

    1. Create an IAM role (sagemaker_role) for SageMaker to be able to execute tasks related to our AI model.
    2. Attach a policy that grants full access to SageMaker (sagemaker_policy_attachment) so that the role can manage model training and deployment.
    3. Set up a SageMaker model (sagemaker_model) with the execution role and the location of the model data.
    4. Use aws.iam.get_policy_document to generate a policy document that permits cross-account model invocation.
    5. Create a role policy (cross_account_policy) attaching the cross-account policy to the SageMaker role.
    6. Export the ARNs of the role and the model, which you may need for setting up cross-account access elsewhere.

    Remember that the actual model and data URLs must be replaced with the URLs where your model and its data are located (i.e., in ECR and an S3 bucket, respectively). Also, be sure to replace <Account B ID> with the account ID that you want to grant access to.

    To fully implement cross-account AI model deployment you will need further automation like Lambda functions, which could automatically create SageMaker endpoints in various accounts and execute updates when the model is retrained. However, this basic setup creates the foundational IAM role and SageMaker model.