1. Alerting and Anomaly Detection for AI Models Using Azure Monitor.


    To set up alerting and anomaly detection for AI (Artificial Intelligence) models using Azure Monitor, we can create configurations for monitoring metrics emitted by the service that hosts the AI models. Azure Monitor allows us to collect, analyze, and act on telemetry data from Azure and on-premises environments.

    Here are the main steps we'll take to set up the monitoring:

    1. Create an Application Insights Component: Application Insights is an extensible Application Performance Management (APM) service for web developers on multiple platforms. We'll use it to monitor our live web application, automatically detecting performance anomalies.

    2. Set Up Anomaly Detection in Application Insights: Configure the built-in anomaly detection capabilities. Application Insights can automatically detect anomalies in the performance and usage patterns of your application.

    3. Create Alert Rules: After setting up anomaly detection, we'll create alert rules to get notified when an anomaly is detected. Azure Monitor Alerts proactively notify us of critical conditions and potentially take automated actions.

    Azure Monitor provides different kinds of alerts, such as metric alerts, log alerts, and activity log alerts. For AI anomaly detection, metric alerts or log alerts can be more relevant.

    Below is a Pulumi program written in Python that sets up an Application Insights Component for monitoring, enabling anomaly detection, and creating an alert rule in Azure Monitor.

    import pulumi import pulumi_azure_native as azure_native # Define the config for the Azure region to deploy resources in location = "East US" # Replace with your Azure region # Create an Application Insights component app_insights_component = azure_native.insights.Component("appInsightsComponent", kind="web", location=location, application_type="web", # More properties can be configured as needed ) # Here, we can configure Application Insights to enable smart detection which is a feature that proactively identifies performance anomalies. # Due to the limitations of current Pulumi's azure-native provider, setting up Smart Detection rules is not supported through code. # However, you could manually configure Smart Detection through the Azure Portal under the 'Application Insights Component' settings. # Create a metric alert rule for monitoring the performance of the AI model, e.g., response time. metric_alert_rule = azure_native.insights.MetricAlertResource("metricAlertRule", location=location, resource_group_name=app_insights_component.resource_group_name, description="Alert when AI model performance degrades", severity=3, enabled=True, scopes=[app_insights_component.id], evaluation_frequency="PT1M", # Evaluates every minute window_size="PT15M", # 15-minute window for evaluation criteria=azure_native.insights.MetricAlertSingleResourceMultipleMetricCriteriaArgs( all_of=[ azure_native.insights.MetricCriteriaArgs( name="HighResponseTime", # Name of the criterion metric_name="request/responseTime", # Metric to monitor operator="GreaterThan", threshold=1000, # Threshold value for the response time in ms time_aggregation="Average", ) ], ), ) # The alert created above will monitor the response time of the AI model hosted on a service that integrates with Application Insights. # If the average response time goes above 1000 ms in a 15-minute window, the alert will be triggered. # The severity level is set to 3, which can be adjusted according to the preferences ranging from 0 (critical) to 4 (verbose). # Export the Application Insights Instrumentation Key for use in the application pulumi.export('app_insights_instrumentation_key', app_insights_component.instrumentation_key) # Note: To complete the integration, you would need to configure your AI model's hosting service to send telemetry to the Application Insights instance. # You would typically use the instrumentation key exported above in your application's settings.

    In the example above, we used the azure_native.insights.Component resource to create an Application Insights component in the specified Azure region. This component will be responsible for tracking the application telemetry, which includes performance metrics, errors, and usage data. Then we utilized the azure_native.insights.MetricAlertResource to set up an alert rule based on specific criteria that we consider indicative of an anomaly or performance issue.

    Once this setup is deployed, you can use the Application Insights instance to monitor your AI application. You'll be able to define more sophisticated anomaly detection and alerting mechanisms directly in the Azure Portal if you need more detailed configuration. The alerting mechanism established here ensures that you are notified promptly if the AI model's performance deviates from expected thresholds, allowing for immediate investigation and resolution.