1. Distributed Tracing for AI-powered Applications with Azure Application Insights

    Python

    Distributed tracing is a method used to track the progress and performance of requests as they travel across various services and components of an application. For AI-powered applications running on Azure, Application Insights is an extensible Application Performance Management (APM) service that can be used to monitor live applications, detect performance anomalies, and track the outcomes of distributed tracing.

    To set up distributed tracing for an AI-powered application using Azure Application Insights, you will typically need to:

    1. Create an instance of Azure Application Insights to collect telemetry from the application.
    2. Instrument your application code by integrating the Application Insights SDK, which will automatically collect and send telemetry data including distributed trace information.
    3. Query and visualize the telemetry data in the Azure portal to gain insights into the application's performance and operation.

    Below is a Pulumi program that sets up an instance of Azure Application Insights for distributed tracing:

    import pulumi import pulumi_azure_native as azure_native # Create a resource group where all the resources related to Application Insights will reside. resource_group = azure_native.resources.ResourceGroup("ai_resource_group") # Create an instance of Azure Application Insights for the application. app_insights = azure_native.insights.Component("appInsightsComponent", resource_group_name=resource_group.name, kind="web", location="East US", # Replace with the desired location for your Application Insights resource. application_type=azure_native.insights.ApplicationType.WEB, ) # Export the Instrumentation Key, which will be used in the application to send telemetry data to Application Insights. pulumi.export('instrumentation_key', app_insights.instrumentation_key) # This program requires a package dependency on `pulumi_azure_native`.

    Explanation

    In the program:

    • A Resource Group is created as a container for organizing the Azure Application Insights resources within your Azure subscription.
    • An Application Insights resource is instantiated using the azure_native.insights.Component class. This resource will collect telemetry, including distributed tracing information.
    • The kind parameter is set to "web", which is suitable for back-end services, web applications, and other related workloads.
    • The application_type parameter is set to ApplicationType.WEB, indicating that the application is a web application.
    • The location parameter denotes the geographic location where the Application Insights resource will be hosted. You should replace "East US" with the location that best suits your requirements.
    • The instrumentation_key attribute from the Application Insights resource is exported. This key is essential for instrumenting your application and will be used to initialize the Application Insights SDK in your application code.

    To complete the setup, you would need to integrate the Application Insights SDK into your AI application. Depending on the programming language and framework used, this step involves adding the appropriate Application Insights package and initializing it with the instrumentation key (obtained from the above Pulumi program output) during the application startup.

    Please refer to the Azure Application Insights documentation for guidance on how to instrument your application with the SDK and enable distributed tracing.

    Remember that this Pulumi program is the infrastructure part of the setup. You will still need to modify your application code to leverage Azure Application Insights fully.


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