1. AI Model Deployment Templates via AWS Service Catalog

    Python

    To deploy AI model deployment templates via AWS Service Catalog, you would typically create a Service Catalog product that encapsulates your AI model deployment infrastructure and configuration. Service Catalog products are essentially pre-defined cloud resource configuration templates that an organization can offer to its users as a managed catalog.

    In AWS, Service Catalog products are defined using AWS CloudFormation templates. When a user wants to deploy an AI model (or any other type of infrastructure), they select a product from the Service Catalog and provision it. This creates an instance of the product in their AWS account, with resources defined by the CloudFormation template.

    Here's a breakdown of how you can create and use a Service Catalog product for AI model deployment using Pulumi in Python:

    1. Create the CloudFormation Template: Write a CloudFormation template that describes the resources needed to deploy the AI model. This could include an Amazon SageMaker endpoint, an AWS Lambda function, API Gateway, and any associated IAM roles and policies.

    2. Create a Service Catalog Product: Use Pulumi to define a Service Catalog product resource. You'll reference the CloudFormation template from step 1 here.

    3. Create a Service Catalog Provisioned Product: When you need to deploy the AI model, use Pulumi to create a provisioned product. This is an instantiation of the product template with the specific values for your AI model deployment.

    To follow this process, you would typically use resources such as aws.servicecatalog.Product to define the product and aws.servicecatalog.ProvisionedProduct to provision an instance of the product.

    Below is a Pulumi program that outlines these steps:

    import pulumi import pulumi_aws as aws # Step 1: Assuming you've already created a CloudFormation template for your AI model deployment, # and it is available as a file named "ai_model_deployment_template.yaml" in your setup. # Step 2: Create a Service Catalog Product product = aws.servicecatalog.Product("aiModelDeploymentProduct", name="AIModelDeployment", owner="data-science-team", description="Product to deploy AI models", product_type="CLOUD_FORMATION_TEMPLATE", tags={ "Department": "DataScience", }, provisioning_artifact_parameters={ "disable_template_validation": False, "info": { "LoadTemplateFromURL": pulumi.Output.all(pulumi.get_stack()).apply( lambda stack: f"https://s3-{stack.region}.amazonaws.com/my-bucket/ai_model_deployment_template.yaml" ) }, "name": "v1.0", }) # Step 3: Create a Service Catalog Provisioned Product to deploy an instance of your product provisioned_product = aws.servicecatalog.ProvisionedProduct("aiModelDeployment", product_id=product.id, provisioning_artifact_id=product.default_provisioning_artifact_id, provisioning_parameters=[{ "key": "ModelName", "value": "my-ai-model" }], # Set additional configuration specific to your AI model deployment if needed ) # Export the provisioned product as output pulumi.export("provisioned_product_name", provisioned_product.name)

    Here's what the code above does:

    • It defines a Service Catalog product (aiModelDeploymentProduct) for your AI model deployment, using a CloudFormation template file that you have uploaded to an S3 bucket. The URL for the template file is generated using the pulumi.get_stack() function which returns the current stack configuration, including the AWS region.

    • It then provisions this product (aiModelDeployment) to create the AI model deployment infrastructure. The provisioning parameters include custom values like the name of the AI model that are needed for the deployment.

    Please make sure to replace my-bucket with the name of your actual S3 bucket and ai_model_deployment_template.yaml with the path to your CloudFormation template file within the S3 bucket. Also, ensure you have the correct permissions set up for Service Catalog to access the CloudFormation template in S3.

    For more information about the resources used above, you can check the AWS Service Catalog documentation:

    Remember also to configure AWS credentials for Pulumi to interact with your account. And keep in mind that administrative permissions are required for managing Service Catalog products and provisioning resources.


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