1. Tracking AI Application Failures with Azure Application Insights


    To track AI application failures with Azure Application Insights, we will set up an instance of Application Insights, which is an extensible Application Performance Management (APM) service for web developers on multiple platforms. It allows you to monitor your live applications by automatically detecting performance anomalies, tracking work items, and providing powerful analytics tools.

    Here's what we will do:

    1. Set up an instance of Azure Application Insights using the Component resource from azure-native provider. This will provide us with the necessary Application Insights instrumentation key that can be added to the application code to track failures.
    2. Configure the AI application to send telemetry data, including exceptions and failures, to this Application Insights instance. Typically, this involves adding the Application Insights SDK to your application and configuring it with your instrumentation key.

    Below is a Pulumi program in Python that creates an Azure Application Insights resource:

    import pulumi from pulumi_azure_native import insights # Create an Azure Resource Group resource_group = insights.ResourceGroup("resourceGroup", resource_group_name="ai-app-rg") # Create an instance of Azure Application Insights for monitoring the application app_insights = insights.Component("appInsights", resource_name="ai-application", resource_group_name=resource_group.name, kind="web", application_type="web", location="East US") # Export the Instrumentation Key of Application Insights pulumi.export('instrumentation_key', app_insights.instrumentation_key)

    The insights.Component is the fundamental resource provided by the Azure Native provider for creating an instance of Application Insights. Here are some important points about the configuration:

    • The resource_group_name specifies the name of the resource group that will hold our Application Insights resource. Here, we're generating a resource group with the name "ai-app-rg".
    • The kind indicates the type of application that will be monitored. In this example, it's a web application, indicated by the "web" kind.
    • The application_type helps Azure Application Insights understand the application better and provides tailored analysis. "web" indicates that it is a web application.

    After you deploy the program, the instrumentation_key (which is necessary to instrument your application) is exported so that you can use it in your application's telemetry configuration.

    To use this Instrumentation Key, you would typically integrate it within your AI application's code using the Application Insights SDK for your programming language. For example, in a Python Flask application, you would use:

    from applicationinsights.flask.ext import AppInsights app.config['APPLICATION_INSIGHTS_KEY'] = 'your_instrumentation_key' appinsights = AppInsights(app)

    Remember to replace 'your_instrumentation_key' with the actual key provided by Pulumi after the deployment.

    The SDK will then automatically collect various telemetry data, including requests, exceptions, dependencies, traces, and metrics. It will also track unhandled exceptions and performance data to give you insights into how your application is performing and where it's failing.

    Make sure your AI application is configured to send failure and exception telemetry, which will then be visible in the Azure Portal under your Application Insights resource. You can drill down into the failures to understand what's causing them and take corrective actions.