1. Feature Toggling for AI Model Deployment with AWS AppConfig

    Python

    Feature toggling is a powerful technique that allows developers to turn on or off certain features of an application without deploying new code. This can be extremely useful for controlled rollouts, A/B testing, and quickly disabling features in production if issues arise. AWS AppConfig is a service that helps you manage configurations for your applications and enables controlled feature deployments.

    To implement feature toggling for AI model deployment using AWS AppConfig, we’ll set up an AppConfig application, configuration profile, and environment. We'll also define a deployment strategy to manage how quickly the new configuration propagates through the environment. Then, we’ll create an AppConfig extension to associate custom actions or validations with the AppConfig resource lifecycle (e.g., validating the configurations), and finally deploy the configuration.

    Below is a Pulumi program in Python that sets up these different components using the AWS Pulumi provider, which you can customize for your specific use case:

    import pulumi import pulumi_aws as aws # Create an AppConfig application that will contain all the other resources app = aws.appconfig.Application("aiModelTogglingApp", # Description is optional but recommended to identify the application's purpose description="Application for AI model feature toggling") # Create a configuration profile within the application # This profile will have information about your configurations config_profile = aws.appconfig.ConfigurationProfile("aiModelTogglingConfigProfile", application_id=app.id, location_uri="hosted", # AWS hosts the configuration data description="Configuration profile for AI model feature toggling") # Create an environment within the application # Environments are logical deployment groups like 'testing', 'production', etc. environment = aws.appconfig.Environment("aiModelTogglingEnvironment", application_id=app.id, description="Environment for AI model feature toggling") # Define a deployment strategy for the application configuration deployment_strategy = aws.appconfig.DeploymentStrategy("aiModelTogglingDeploymentStrategy", # Time in minutes for the deployment to complete deployment_duration_in_minutes=30, # The percentage of targets to receive the deployed configuration during each interval growth_factor=10, description="Deployment strategy for AI model feature toggling", # Apply changes linearly or exponentially growth_type="LINEAR", # How quickly AppConfig monitors and alarms are evaluated replicate_to="SSM_DOCUMENT") # Create an AppConfig extension to associate with our AppConfig resources # Extensions let you extend the AppConfig resource lifecycle using Lambda functions or CloudWatch alarms extension = aws.appconfig.Extension("aiModelTogglingExtension", description="Extension for AI model feature toggling", # Define actions to run on certain lifecycle points of AppConfig action_points=[aws.appconfig.ExtensionActionPointsArgs( point="PRE_CREATE_ENVIRONMENT", actions=[aws.appconfig.ExtensionActionsArgs( description="Validate configuration before creating environment", name="MyValidationLambda", uri="arn:aws:lambda:region:account-id:function:my-lambda-validator", role_arn="arn:aws:iam::account-id:role/service-role/role-for-my-lambda-validator", )], )]) # Deployment of the configuration deployment = aws.appconfig.Deployment("aiModelTogglingDeployment", application_id=app.id, environment_id=environment.id, configuration_profile_id=config_profile.id, configuration_version="1", # The version of the configuration to deploy deployment_strategy_id=deployment_strategy.id, description="Deployment for AI model feature toggling") # Exporting the URLs and IDs so they may be accessed easily outside of this Pulumi program pulumi.export('application_id', app.id) pulumi.export('configuration_profile_id', config_profile.id) pulumi.export('environment_id', environment.id) pulumi.export('deployment_strategy_id', deployment_strategy.id)

    This program creates all the necessary components to manage feature toggles for your AI model deployment. Here’s an overview of each part of the program:

    • Application: This is the container for all the other AppConfig resources. It can represent your entire application or a particular service within your application.
    • Configuration Profile: This holds information about your configuration data. Setting location_uri="hosted" means AWS AppConfig will host the configuration data.
    • Environment: Defines a logical group where configurations are deployed, such as testing or production.
    • Deployment Strategy: Specifies how the configuration changes are rolled out. For example, it specifies the rate (growth factor) and manner (growth type) of deployment.
    • Extension: Enables custom actions, like invoking Lambda functions for validation, during the AppConfig lifecycle.
    • Deployment: Triggers the deployment of the configuration according to the defined strategy and configuration profile.

    For more information, refer to the AppConfig Application, Configuration Profile, Environment, Deployment Strategy, and Extension documentation.


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