1. Azure Application Insights for AI Incident Management


    Azure Application Insights is a feature of Azure Monitor that provides extensive logging, performance monitoring, and complex analysis capabilities for applications. It collects telemetry data from your application, including requests, exceptions, dependencies, and performance counters, and uses powerful analytics tools to help you detect issues, diagnose exceptions, and track the resolution of incidents.

    Here's how you can use Pulumi to configure Azure Application Insights for AI Incident Management:

    1. Create an Application Insights component: This will be the central place where all telemetry from your application will be collected.
    2. Configure AnalyticsItem: To define custom analytics queries, alerts, and other configuration items.
    3. Set up Alert Rules: To monitor your application's telemetry and fire incidents when criteria you define are met.
    4. Integrate with other Azure services: Depending on your incident management process, you might want to integrate Application Insights with Azure Logic Apps, Functions, or Azure Action Groups to automate responses to incidents.

    Pulumi Python Program for Azure Application Insights

    Below is a Pulumi program in Python that sets up Azure Application Insights for AI Incident Management:

    import pulumi import pulumi_azure_native as azure_native # Configuration app_name = "my-application" location = "West US" resource_group_name = "my-resource-group" # Create an Azure resource group resource_group = azure_native.resources.ResourceGroup("resource_group", resource_group_name=resource_group_name, location=location ) # Create an Application Insights component app_insights = azure_native.insights.Component("appInsights", resource_name=app_name, location=resource_group.location, resource_group_name=resource_group.name, kind="web", application_type=azure_native.insights.ApplicationType.WEB, ) # Example query for Azure Application Insights AnalyticsItem analytics_query = """ exceptions | summarize count() by problemId | top 5 by count_ """ # Create an Analytics item to store a query analytics_item = azure_native.insights.AnalyticsItem("myAnalyticsItem", resource_group_name=resource_group.name, resource_name=app_insights.name, content=analytics_query, scope_path="/", name="TopExceptions", type="query" ) # Pulumi stack export to output the Instrumentation Key of Application Insights, # which is required for configuring the SDK in your application pulumi.export("instrumentation_key", app_insights.instrumentation_key)


    • Resource Group: Azure resources must be placed in a resource group, so we create one with the name my-resource-group in a specified location (West US in this case).

    • Application Insights Component: This creates an Application Insights instance within the resource group. The application_type indicates what type of app you are monitoring—in this case, a web application.

    • Analytics Item: This creates a saved query inside Application Insights. You can define your custom queries for frequent use; in the example, we're summarizing exceptions and getting the top 5 by count. This is where you'd likely put queries specific to incident management.

    • Stack Exports: At the end of our Pulumi program, we export the instrumentation_key of our Application Insights component, which is provided by the Azure resource and is necessary to configure the Application Insights SDK in your application code.

    This program focuses on foundational steps to configure Application Insights. In a real-world setup, you'd likely have additional configurations such as alert rules, which would trigger incidents based on the telemetry data, and potentially workflows or functions to manage those incidents. Automation around incident response can be set up using other Azure services, such as Azure Logic Apps or Azure Functions, to create a comprehensive AI Incident Management solution.