1. Monitoring ML Model Performance with Azure Dashboards


    To create a monitoring solution for ML model performance using Azure Dashboards, you can utilize various services offered by Azure. A common approach involves sending custom metrics from your application to Azure Monitor, which can then be displayed on a dashboard for easy visualization and tracking.

    Azure Monitor collects and aggregates data from different sources into a common data platform where it can be used for analysis, visualization, and alerting. The types of data available for monitoring include metrics, logs, and traces.

    For the purpose of monitoring ML model performance, you would typically send custom metrics from your application that represents the performance of the ML model, such as prediction accuracy, recall, precision, or any other relevant KPIs (Key Performance Indicators) to Azure Monitor using the Application Insights SDK.

    To present these metrics, you create a dashboard within the Azure portal, or use services like Azure Grafana for more complex visualizations. Below we will create an Azure Dashboard with a simplified version of the required resources using Pulumi in Python.

    We will use the following resources:

    • Azure Monitor for collecting and analyzing performance metrics.
    • Application Insights for application performance monitoring.
    • Azure Dashboard for displaying the metrics on a custom dashboard.

    Here's a Pulumi program that demonstrates how you would define these resources using Python:

    import pulumi import pulumi_azure_native as azure_native # Define the resource group resource_group = azure_native.resources.ResourceGroup("resource_group") # Define an Application Insights instance which will be used to monitor the application app_insights = azure_native.insights.Component("appInsights", resource_group_name=resource_group.name, kind="web", application_type="web") # Define the Azure Dashboard dashboard = azure_native.portal.Dashboard("dashboard", resource_group_name=resource_group.name, location=resource_group.location, tags={"environment": "production"}, lenses=[{ "order": 1, "parts": [ # Here you would define the actual visualization parts # For example, it could be a Metric or a Chart part # that shows your ML model's performance based on the # data from Application Insights { "position": { "x": 0, "y": 0, "colSpan": 3, "rowSpan": 4 }, # The content below should be customized based # on the metrics you want to display on your dashboard "metadata": { "inputs": [ # Inputs for visualizing the specific ML metrics ], "settings": { "content": { "version": "1.0", "type": "Extension/Microsoft_OperationsManagementSuite_Workspace/PartType/ViewDesignerPart", "settings": { # More settings can be configured as needed } } } } }, ] }]) # Export the dashboard URL so it can be accessed easily after deployment pulumi.export("dashboard_url", pulumi.Output.concat( "https://portal.azure.com/#resource/subscriptions/", pulumi.config.require("azure:subscriptionId"), "/resourceGroups/", resource_group.name, "/providers/Microsoft.Portal/dashboards/", dashboard.name) )


    1. A resource group resource_group is created as a container that holds related resources for an Azure solution.

    2. app_insights represents an instance of Application Insights, which collects telemetry data for monitoring the application's performance, including the performance of your ML model.

    3. dashboard is an Azure Dashboard resource where you create a visual representation of your metrics. You would define parts (the commented section within parts) which are the visual components of your dashboard. Each part can be a chart, graph, list, or other supported formats that display your model's performance metrics.

    4. Finally, we're exporting the URL of the dashboard for easy access after it has been deployed.

    Remember to replace the placeholders with actual values for the inputs and settings to match the specific metrics you are tracking for your ML model performance. The dashboard can be customized extensively based on your visualization needs. This program provides a scaffold to create a simple setup On Azure for monitoring ML models.