1. Parameter Management for AI Model Configurations with AWS SSM


    AWS Systems Manager Parameter Store provides a centralized store to manage your configuration data, whether plain-text data such as database strings or secrets such as passwords. It can be particularly useful for managing configurations for AI models where you need to regularly update hyperparameters, connection strings, or model weights.

    In the following Pulumi Python program, we'll use the AWS Systems Manager (SSM) Parameter Store to manage configurations for an AI model. We'll create SSM parameters that might represent different configuration aspects of an AI model such as learning rate, data paths, or credentials for accessing data.

    Let's write a Pulumi program that creates various parameters in AWS SSM:

    import pulumi import pulumi_aws as aws # Create a standard string parameter in SSM for AI model's learning rate learning_rate_param = aws.ssm.Parameter("learningRateParam", name="ai_model_learning_rate", type="String", value="0.01" # Hypothetical value for the learning rate ) # Create a secure string parameter in SSM for database credentials # Note: To use a secure string, specify an AWS KMS key ID db_credentials_param = aws.ssm.Parameter("dbCredentialsParam", name="ai_model_db_credentials", type="SecureString", key_id="alias/aws/ssm", # This uses the default KMS key for SSM parameters value="secure-credentials" # The actual credentials would be a secure string ) # Create a string list parameter in SSM for data source paths data_sources_param = aws.ssm.Parameter("dataSourcesParam", name="ai_model_data_sources", type="StringList", value="path/to/data1,path/to/data2" # Comma-separated paths to data sources ) # Output the names of the created parameters so that they can be easily identified pulumi.export("learning_rate_parameter", learning_rate_param.name) pulumi.export("db_credentials_parameter", db_credentials_param.name) pulumi.export("data_sources_parameter", data_sources_param.name)

    In this program, we have created three different types of parameters:

    1. learning_rate_param: A standard string parameter that might represent a simple numeric hyperparameter for an AI model.
    2. db_credentials_param: A secure string parameter that is used to store sensitive information. Secure strings are encrypted with AWS KMS, and this example uses the default /alias/aws/ssm key. You would typically store API keys, database credentials, or other sensitive information in these types of parameters.
    3. data_sources_param: A string list parameter that might represent a list of paths to the data sources required by an AI model.

    After running this program with Pulumi, you will have these parameters stored safely in AWS Systems Manager. You can then reference these parameters in your AI applications using the AWS SDK, allowing you to externalize configuration and sensitive information from your application code.

    To deploy and manage the infrastructure, you would run pulumi up in the command line after setting up your AWS credentials and configuring Pulumi with your desired AWS region. The output would display the names of the parameters which you could note for reference.

    Remember to restrict access to sensitive parameters by appropriate AWS Identity and Access Management (IAM) policies, and audit access and usage as necessary, ensuring they are used securely and compliantly within your organization.