1. Anomaly Detection in AI Workloads with Azure Insights Alerts


    To set up anomaly detection in AI workloads with Azure Insights alerts, we'll leverage two Azure services: Azure Application Insights and Azure Monitor. Azure Application Insights provides powerful APM (Application Performance Management) features for live web applications. It keeps track of your application's health and enables you to monitor performance and user behavior trends. When combined with Azure Monitor's alerting capabilities, you can set up anomaly detection to monitor your AI workloads and get alerted on unusual activities or performance issues.

    Here's a high-level explanation of the tasks we'll perform in the Pulumi Python program:

    1. Azure Application Insights Component: We will create an Application Insights resource, which is the necessary component to collect telemetry and other metrics from the AI workloads.

    2. Azure Monitor Metric Alert Rule: This rule will be set up to trigger alerts based on the anomalies in the metrics collected by Application Insights. The alert rule will monitor specific metrics to assess the health of the AI application and send a notification if an anomaly is detected.

    Let's dive into the Pulumi Python program that accomplishes these tasks:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group which will contain all of the resources. resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Application Insights component for monitoring the AI workload. app_insights = azure_native.insights.Component("appInsights", resource_group_name=resource_group.name, # The type of application being monitored. application_type=azure_native.insights.ApplicationType.WEB, # The location of your resource group. You can choose the nearest Azure region. location=resource_group.location, # Configure any other properties relevant to the application you're monitoring. ) # Now let's define an Alert rule for anomaly detection. # Note: To create a meaningful anomaly detection rule, you would typically specify more detailed criteria about what metric # would be considered an anomaly. You'd also define actions to take when an anomaly is detected, such as sending an email notification. # For simplicity, this example uses a placeholder metric and does not define an action group. anomaly_alert_rule = azure_native.insights.MetricAlertResource("anomalyAlertRule", resource_group_name=resource_group.name, location=app_insights.location, criteria=azure_native.insights.MetricAlertSingleResourceMultipleMetricCriteriaArgs( all_of=[ azure_native.insights.MetricCriteriaArgs( name="HighCpu", # Name for this criteria metric_name="ProcessorTime", # The metric we are monitoring, in this case, CPU time operator="GreaterThan", # Condition to trigger the alert threshold=80, # Threshold to trigger the alert time_aggregation="Average", # Aggregation for the metric # Specify the time window for evaluating the metrics, frequency for alerts, and other necessary settings ), # Add additional criteria as needed for monitoring other metrics relevant to your AI workloads. ], ), actions=[] # We would normally specify an action group here for notifications. ) pulumi.export("app_insights_instrumentation_key", app_insights.instrumentation_key) pulumi.export("anomaly_alert_rule_id", anomaly_alert_rule.id)

    In this program:

    • We started by creating a resource group to organize our resources.
    • We then set up an Application Insights component specifically configured to monitor a web application's health, but you could configure it based on your specific AI workload needs.
    • We defined an alert rule that employs anomaly detection through the MetricAlertResource. While we've added a simple CPU time metric for demonstration purposes, you'd typically monitor metrics more relevant to your AI application. The alert rule is flexible, and you can define multiple metrics and criteria.
    • Finally, we exported the Application Insights instrumentation key, which you'd use to integrate your application with Application Insights, and the ID of the anomaly alert rule.

    Keep in mind that for a real-world scenario, you would need to define actionable responses in the actions parameter of the alert rule, such as sending notifications via email, calling a webhook, or initiating Azure Functions. Further, you'd need to deploy the telemetry collection agent or SDK inside your AI workload to send metrics to Application Insights.