1. Synthetic Monitoring for AI Services with Azure Web Tests


    Synthetic monitoring is a powerful feature that allows you to simulate user interactions with your application to preemptively discover and diagnose issues. Azure provides synthetic monitoring capabilities using Web Tests under the Application Insights service. These tests allow you to regularly send web requests to your service endpoints, checking availability, response time, and the correctness of content returned by the application.

    Below is a Pulumi program in Python that sets up a synthetic monitoring solution for AI Services using the Azure App Insights Web Test resource. The program will demonstrate how to create an Application Insights instance and a synthetic web test that sends requests to a given URL at regular intervals.

    Firstly, we create an instance of Application Insights, which is a service that allows for monitoring the performance and availability of your web applications. Then, using the WebTest resource, we define a web test that sends HTTP requests to a predefined URL to simulate access by a user or service.

    Let's dive into the Pulumi program:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group to organize our resources resource_group = azure_native.resources.ResourceGroup("rg") # Create an instance of Azure Application Insights for monitoring app_insights = azure_native.insights.components.Component( "appInsights", resource_group_name=resource_group.name, kind="web", # The kind of Application Insights to create; "web" is for web apps application_type="web", # Application type; "web" is for web apps and services location=resource_group.location, # Location should match the resource group ) # Define a Web Test for Synthetic Monitoring web_test = azure_native.insights.WebTest( "syntheticMonitor", location="eastus2", # Define the location to run the test from enabled=True, # The test is enabled to run frequency=300, # Frequency of the test in seconds (300 seconds = 5 minutes) kind="ping", # The kind of test to create; "ping" simply checks if the endpoint is available locations=[ # Define multiple test locations if required azure_native.insights.WebTestGeolocationArgs( location="us-tx-sn1-azr" # An example location, actual value should be a valid Azure location ), ], profile=azure_native.insights.WebTestPropertiesProfileArgs( retry_enabled=True, ), request=azure_native.insights.WebTestPropertiesRequestArgs( request_url="https://myservice.com/api/endpoint", # Your AI service's endpoint URL ), resource_group_name=resource_group.name, synthetic_monitor_id="my_synthetic_monitor", # A unique identifier for the synthetic monitor validation_rules=azure_native.insights.WebTestPropertiesValidationRulesArgs( expected_http_status_code=200, # The expected HTTP status code to assert the test as successful content_validation=azure_native.insights.WebTestPropertiesValidationRulesContentValidationArgs( content_match="Service is up", # The content to match in the response pass_if_text_found=True, # Pass the test if the text is found ) ) ) # Output the Application Insights Instrumentation Key pulumi.export("instrumentation_key", app_insights.instrumentation_key) # Output the Web Test ID pulumi.export("synthetic_monitor_id", web_test.synthetic_monitor_id)

    In this code:

    • We set up an Azure resource group to hold all related resources.
    • We create an Application Insights instance, which is used for monitoring the application's health and traffic data.
    • We define a WebTest object that represents a test accessing an AI service URL every 5 minutes, checking for availability and correct content response.
    • We export two important pieces of information: the Instrumentation Key, which is used to connect application monitoring data to this instance of Application Insights, and the Synthetic Monitor ID, which can help us to reference and manage the web test.

    Don't forget to replace https://myservice.com/api/endpoint with the URL of the AI service you wish to monitor. The expected_http_status_code and content_match values can be customized as well, representing the conditions for a successful test.

    After deploying this program, Pulumi will provision these resources in Azure and begin monitoring as per the defined web test. If appInsights detects any issues according to the rules we've set in web_test, you can configure alerts in the Azure portal to notify you.

    The output includes the instrumentation_key, which is necessary for the integration of various SDKs with Application Insights services, allowing us to send telemetry data to App Insights. The synthetic_monitor_id can be used to uniquely identify the web test in logs or when configuring alerts for the test.

    This program provides a straightforward way to set up synthetic monitoring and ensure that your AI services are available and performing as expected without manual oversight.