1. AI Application Telemetry with Azure Application Insights


    Azure Application Insights is a feature of Azure Monitor that provides extensible Application Performance Management (APM) and monitoring services for web applications. By integrating Application Insights, you can track your applications' live operations, detect performance anomalies, and track usage patterns to improve your application's performance and usability.

    To start monitoring your application with Azure Application Insights using Pulumi, you need to create an instance of Application Insights and configure your application to send telemetry to that instance.

    Below is a Pulumi program in Python that creates a new instance of Application Insights. We will use the azure-native provider for this purpose because it provides direct access to Azure APIs and is being actively developed with support for the latest features of Azure.

    import pulumi import pulumi_azure_native.insights as insights # Configuration variables for the Application Insights resource. app_insights_name = "myAppInsights" resource_group_name = "myResourceGroup" location = "West US 2" # Create an Azure Resource Group. resource_group = insights.ResourceGroup("resource_group", resource_group_name=resource_group_name, location=location) # Create an instance of Azure Application Insights. app_insights = insights.Component("appInsights", resource_name=app_insights_name, resource_group_name=resource_group.name, location=location, # Set the type of application you are monitoring, e.g., "web", "other", etc. application_type=insights.ApplicationType.WEB, kind="web", # This specifies that we are creating a web-based Application Insights. ) # Export the Instrumentation Key of Application Insights which is used to configure the SDK. pulumi.export("instrumentation_key", app_insights.instrumentation_key)

    This program performs the following actions:

    1. It imports the necessary Pulumi and Azure-native modules.
    2. It defines configuration variables for naming and location purposes.
    3. It initializes an Azure Resource Group, which is a container for Azure resources. The Application Insights resource will be a part of this group.
    4. It creates an Application Insights component with the type set to 'web', assuming you are going to monitor a web application. The kind argument also emphasizes that it's a web-based insight.
    5. It exports the instrumentation_key, which you can then use in your application to send telemetry data to Azure Application Insights.

    Once you have the instrumentation key, you should integrate the Application Insights SDK with your application. This will vary depending on the type of application you're developing (ASP.NET, Node.js, Java, etc.). Ensure you follow the Microsoft documentation to configure the Application Insights SDK in your app codebase properly.

    Remember that the application should be deployed to a platform where Application Insights can effectively monitor it, typically in Azure, but it can be anywhere as long as it has internet access to send telemetry data.

    To run this Pulumi program:

    1. Set up your Azure account with the az login.
    2. Install Pulumi and set up the Python environment.
    3. Save this code in a file, for instance, main.py.
    4. Run pulumi up to deploy the program which will set up Application Insights in the specified Azure location.