1. Proactive Anomaly Detection in AI Workloads using Azure AppInsights


    To set up proactive anomaly detection in AI workloads with Azure Application Insights, you can leverage several Azure resources such as Application Insights for monitoring the workload, smart detection rules for proactive detection, and web tests for availability monitoring. This will help you to monitor the performance and usage of your application, set up proactive alerts, and detect anomalies in real-time which may indicate performance problems or other issues.

    Here's how you'd set this up with Pulumi in Python:

    1. Application Insights Component: This is the central piece of the Application Insights service, where telemetry from your application will be sent for analysis and querying.

    2. Smart Detection Rule: These rules allow Application Insights to automatically detect performance anomalies, failure anomalies, and other irregular patterns in your application's telemetry.

    3. Web Test: Regularly sends web requests to your application to check its availability and track its response time and success rate.

    Below is a program written in Python using Pulumi, which will create an Application Insights component for monitoring an application, along with a smart detection rule to proactively detect anomalies, and a web test for availability. Remember to replace "your_resource_group_name", "your_app_insights_name", "your_web_test_name" and other placeholders with your actual resource identifiers and desired configuration.

    import pulumi import pulumi_azure_native as azure_native # Replace these variables with your own values resource_group_name = "your_resource_group_name" app_insights_name = "your_app_insights_name" app_type = "web" location = "West US" web_test_name = "your_web_test_name" app_insights_web_test_url = "http://example.com" # The URL to monitor # Create an Application Insights component for monitoring app_insights_component = azure_native.insights.Component("appInsightsComponent", resource_name=app_insights_name, resource_group_name=resource_group_name, kind='web', location=location, application_type=app_type) # Output the Instrumentation Key for the Application Insights component pulumi.export('appInsightsInstrumentationKey', app_insights_component.instrumentation_key) # Create a Smart Detection Rule for proactive anomaly detection smart_detection_rule = azure_native.insights.ComponentProactiveDetectionConfiguration("smartDetectionRule", configuration_name="FailureAnomalies", # or use another detection type resource_group_name=resource_group_name, component_name=app_insights_name, send_emails_to_subscription_owners=True, # Set to 'False' if you don't want emails sent to owners custom_emails=["your_email@example.com"], # Add additional email addresses if needed enabled=True) # Set to 'False' to disable the rule # Create a Web Test for availability monitoring web_test = azure_native.insights.WebTest("webTest", name=web_test_name, location=location, resource_group_name=resource_group_name, kind='ping', # Simplest test that sends a ping to your application enabled=True, frequency=300, # Frequency at which the test should run, in seconds (e.g., 300 seconds) timeout=120, # Timeout in seconds after which the test fails web_test_kind="ping", # Keep this as ping for a simple ping test retry_enabled=True, locations=[ # Locations from which the test will be run azure_native.insights.WebTestGeolocationArgs( location="us-ca-sjc-azr" ), azure_native.insights.WebTestGeolocationArgs( location="us-va-ash-azr" ), ], web_test_name=web_test_name, request=azure_native.insights.WebTestPropertiesWebRequestArgs( request_url=app_insights_web_test_url # URL to be pinged )) # Output the Web Test ID pulumi.export('webTestId', web_test.id)

    When you run the Pulumi program above, the following will happen:

    • Pulumi will connect to Azure and create an Application Insights component within the specified resource group and location.
    • A Smart Detection Rule will be set up on the Application Insights Component that triggers alerts based on certain anomaly detection patterns, such as failure anomalies.
    • A Web Test will ping your specified URL at regular intervals to check its availability while pushing that telemetry back into Application Insights for tracking and analysis.

    This setup will help you proactively monitor your AI workloads and detect anomalies using Azure Application Insights. If you need to customize the anomaly detection further, you can adjust the parameters of the smart detection rule, or use the Analytics solutions within Application Insights to query and visualize the telemetry data captured by your application.