1. Compliance Monitoring for Machine Learning Infrastructure with AWS Config


    Compliance monitoring in the context of infrastructure as code (IaC) refers to the process of continuously tracking and managing changes in the cloud infrastructure to ensure they meet the organization's compliance standards and policies. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources.

    To implement compliance monitoring for machine learning infrastructure using AWS Config, you typically need to:

    1. Define AWS Config rules that relate to the compliance requirements for the machine learning infrastructure.
    2. Configure AWS Config to monitor the changes in the resources and evaluate them against the defined rules.
    3. Optionally, set up notifications or remediation actions based on the compliance evaluation results.

    Let's say you want to ensure that all your Amazon Sagemaker notebook instances are using a certain instance type that complies with your company’s standards.

    Here's a Pulumi Python program that creates an AWS Config rule to check for the compliance of Sagemaker notebook instances:

    import pulumi import pulumi_aws as aws # Define an AWS Config rule to monitor the instance type of Sagemaker notebook instances. sagemaker_instance_type_rule = aws.cfg.Rule("sagemakerInstanceTypeRule", name="sagemaker-notebook-instance-type", source=aws.cfg.RuleSourceArgs( owner="AWS", source_identifier="SAGEMAKER_NOTEBOOK_INSTANCE_TYPE_CHECK", ), input_parameters=pulumi.Output.all("{\"desiredInstanceType\":\"ml.t2.medium\"}"), scope=aws.cfg.RuleScopeArgs( compliance_resource_types=["AWS::Sagemaker::NotebookInstance"] ), description="Ensure all Sagemaker notebook instances are of type ml.t2.medium." ) # Export the ID of the rule for reference. pulumi.export("config_rule_id", sagemaker_instance_type_rule.id)

    In this program:

    • We import the required Pulumi modules.
    • We create an instance of aws.cfg.Rule. This represents an AWS Config rule resource that defines the compliance checks.
    • The source argument specifies that we are using a managed rule provided by AWS, identified by source_identifier.
    • input_parameters takes a JSON string that contains the parameters we want to enforce; here we ensure that the instance type is ml.t2.medium.
    • scope specifies the AWS resource type that the rule applies to. Here, it applies to AWS Sagemaker Notebook Instances.
    • We add a description to document the purpose of this rule.
    • Finally, we export the ID of the AWS Config rule for reference or use in other parts of our infrastructure.

    By running this Pulumi program, you set up an AWS Config rule that continuously monitors your AWS Sagemaker notebook instances to check if they are compliant with your specified instance type requirement. If non-compliant resources are detected, AWS Config flags them so you can review and take appropriate action.