1. Parameter Store for AI Model Configuration Management


    To manage configuration for an AI model using a Parameter Store, you can utilize the AWS Systems Manager (SSM) Parameter Store, which allows you to securely store configuration data and secrets. Here's how you can accomplish this with Pulumi using Python.


    The SSM Parameter Store provides a centralized place to store your configuration and secrets, which can be programmatically retrieved from your applications or services. This kind of setup is essential for managing AI models that might require specific configurations, hyperparameters, or secrets that you do not want to hardcode in the application code or check into source control.

    In Pulumi, you can use the aws.ssm.Parameter resource to create and manage parameters within the AWS SSM Parameter Store. This resource allows you to specify various attributes of the parameter, such as the name, type (String, StringList, SecureString), and the actual value.

    For sensitive data like database credentials or API keys that should be kept secret, you should use the SecureString type. This encrypts the parameter value using a key from AWS Key Management Service (KMS).

    Pulumi Program

    Here is a Pulumi program that sets up a simple SSM Parameter Store with one parameter for AI model configuration. I'll add comments to explain each step.

    import pulumi import pulumi_aws as aws # Create a KMS key for encrypting your secrets kms_key = aws.kms.Key("my-key", description="KMS key for encrypting SSM Parameter Store values", policy=""" { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": {"AWS": "arn:aws:iam::ACCOUNT_ID:root"}, "Action": "kms:*", "Resource": "*" } ] } """ ) # Store an AI model configuration parameter as a SecureString ai_model_config = aws.ssm.Parameter("aiModelConfig", # Referencing the KMS key created above key_id=kms_key.id, # Name of the parameter name="aiModelConfigParameter", # The type of the parameter - SecureString is used for sensitive data type="SecureString", # The value of the parameter - this would be your AI model configuration or secret value="my-secret-model-config", # Use the latest data type data_type="text" ) # To use the parameter in your AI model or application, you would typically retrieve # the parameter value using AWS SDKs or CLI. Here, we're just exporting the name for reference. pulumi.export("aiModelConfigParameterName", ai_model_config.name)

    Retrieving the Stored Parameter

    To retrieve this parameter in your application, you would use the AWS SDK for your preferred programming language. You would look up the parameter by name and, if it’s a SecureString, decrypt it using the appropriate permissions.

    This simple Pulumi program demonstrates how to securely manage your AI model configurations using AWS and Pulumi. You can expand this program to manage multiple parameters, organize them with tags, or integrate with your CI/CD pipeline for automatized deployments and updates.