1. Detecting Anomalies in AI Applications with Azure Monitor


    To monitor and detect anomalies in AI applications using Azure Monitor, we'll create an instance of Azure Monitor Logs via an Azure Log Analytics Workspace. Azure Monitor collects and analyzes data generated by resources in your cloud and on-premises environments. The key component of Azure Monitor for Logs is Log Analytics, which allows you to collect and aggregate data from monitored resources into a central repository for querying and analysis.

    The following Pulumi program sets up an Azure Monitor workspace:

    1. Azure Monitor Logs Workspace: This is the central service for managing all logs within Azure. It provides a querying language, and analytics engine, and a means to collect data from multiple sources for complex data analysis, or for forwarding to another service for further processing.

    2. Alert Rules: We'll create Alert Rules to notify us when there are potential anomalies. Azure Monitor can generate alerts based on metrics or events, indicating issues you might need to investigate.

    Here's a Pulumi program in Python that accomplishes this.

    import pulumi import pulumi_azure_native as azure_native # Configuring the Azure Log Analytics Workspace log_analytics_workspace = azure_native.operationalinsights.Workspace( "logAnalyticsWorkspace", resource_group_name="example-resource-group", # replace with your Azure resource group name workspace_name="exampleworkspace", location="East US", # replace with the appropriate Azure region sku=azure_native.operationalinsights.SkuArgs( name="PerGB2018", # The pricing tier for the Log Analytics Workspace, update accordingly. ), retention_in_days=30, # Data retention period in days, adjust as required. ) # Example of setting up an Alert Rule to detect anomalies alert_rule = azure_native.insights.AlertRule( "anomalyDetectionAlertRule", resource_group_name="example-resource-group", # replace with your Azure resource group name name="AnomalyDetectionAlert", location="global", # Alert rules are global resources in Azure Monitor # The condition that triggers the alert condition=azure_native.insights.AwesomeConditionArgs( odata_type="Microsoft.Azure.Management.Insights.Models.LocationThresholdRuleCondition", failed_location_count=1, window_size="PT5M", # Check every five minutes for anomalies # Operand and operator should be relevant to what constitutes an anomaly. ), # Actions to be performed when the alert rule is triggered actions=[ azure_native.insights.RuleEmailActionArgs( odata_type="Microsoft.Azure.Management.Insights.Models.RuleEmailAction", send_to_service_owners=True, # Notifies the service owners custom_emails=[ "example@example.com", # Add a custom email to receive alerts ], ), ], # Assuming a metric as an example. The criteria should define what you consider an anomaly. ) # Export the Log Analytics Workspace ID pulumi.export('log_analytics_workspace_id', log_analytics_workspace.id)

    Here's what the program does:

    • It declares an instance of a Log Analytics Workspace where the logs and data would be stored.
    • It specifies a Sku for the Workspace which controls billing and capabilities. For instance, PerGB2018 means you pay per GB of data ingressed into the service, and it's a common option for many log analytics users.
    • We have set data to retain for 30 days but this value can be modified based on how long you want to store the logs.
    • It then shows how to create an alert rule. In the condition object, you'll need to determine what constitutes an anomaly. This can be specific to the AI application scenarios and the metrics they report on.

    Replace the placeholders, such as "example-resource-group", "exampleworkspace", "East US", and emails with your own values as appropriate for your setup.

    To make this program function in your environment, you should replace the placeholder values with actual values pertinent to your setup, such as the resource group name, workspace name, and region. You also need to ensure you have the proper permissions set up in Azure to create and manage these resources.

    Once the Log Analytics Workspace is set up, you can start sending logs and metrics from your AI applications and services to it for analysis. If you're using AI features within Azure services (like Azure Machine Learning), they typically have integration options to send their telemetry over to Azure Monitor. If you're working with external services or your own models hosted on virtual machines or containers, you would need to set up agents that collect and forward the data.

    The next step after collecting data is querying and alerting, which requires a solid understanding of the query language and the structure of the data you're collecting. Azure Monitor's Kusto Query Language (KQL) is used for these queries, and you'll need to write queries that effectively detect anomalies in your data.

    Lastly, automated responses to alerts can also be scripted, for instance, to scale resources or initiate failover processes. This can be achieved by attaching automation scripts or Azure Functions to alerts.