1. Real-time AI Telemetry Analysis with Azure Monitor.


    To accomplish real-time AI telemetry analysis using Azure Monitor, we need to set up a couple of Azure resources. Azure Monitor is a comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments. It helps to understand the performance and health of your applications and services. Here's a brief overview of the resources we'll use and why:

    1. Azure Monitor Workspace: This is the basic management unit of Azure Monitor Logs. It provides storage and an administrative boundary for log data. All data collected by Azure Monitor Logs is stored in one or more Workspaces.

    2. Azure Monitor Action Groups: These let us define a reusable set of actions and an escalation path that can be triggered upon certain conditions detected by Azure Monitor, such as sending email or SMS messages, or triggering automation through Azure Functions or Logic Apps.

    3. Azure Monitor Alert Rule: This is where we define the conditions that will trigger the actions in our Action Group. For telemetry analysis, we would configure alert rules that monitor the telemetry for specific conditions that indicate opportunities or threats for the AI model's performance.

    4. Azure Operational Insights Workspaces (formerly Log Analytics): Operational Insights is a feature of Azure Monitor that collects telemetry from a variety of sources and uses the query language from Azure Data Explorer to retrieve and analyze data.

    Here's a Pulumi program written in Python that will create these resources in Azure. This program assumes that you've already set up an Azure account and have the necessary permissions to create resources.

    import pulumi import pulumi_azure_native as azure_native # Replace with your own resource group name resource_group_name = 'ai_telemetry_rg' # Replace with your desired location location = 'East US' # Create a resource group resource_group = azure_native.resources.ResourceGroup('resource_group', resource_group_name=resource_group_name, location=location) # Create an Azure Monitor Workspace for storing logs monitor_workspace = azure_native.operationalinsights.Workspace('monitor_workspace', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.operationalinsights.SkuArgs( name="PerGB2018" )) # Create an Action Group for notifications action_group = azure_native.insights.ActionGroup('action_group', resource_group_name=resource_group.name, location=resource_group.location, group_short_name='telemetryActions', enabled=True) # Create an Alert Rule for real-time telemetry analysis alert_rule = azure_native.insights.AlertRule('alert_rule', resource_group_name=resource_group.name, location=resource_group.location, condition=azure_native.insights.AlertRuleAllOfConditionArgs( all_of=[ azure_native.insights.AlertRuleAnyOfOrLeafConditionArgs( key='count', operator='GreaterThan', value='5', time_aggregation='Total' ) ] ), actions=azure_native.insights.AlertRuleActionsArgs( action_groups=[action_group.id] ), description='AI Telemetry Alert', enabled=True) pulumi.export('workspace_id', monitor_workspace.id) pulumi.export('action_group_id', action_group.id) pulumi.export('alert_rule_id', alert_rule.id)

    This program sets up the base infrastructure for real-time AI telemetry analysis. The monitor_workspace is where all the AI telemetry logs will be stored. The action_group is used to define how you want to respond to specific alerts, and the alert_rule is the rule that will trigger the action_group if certain conditions in the telemetry data are met.

    After deploying this infrastructure, you would typically configure data sources to send telemetry to the Azure Monitor Workspace and then write queries to analyze that data. For example, you might use Application Insights to collect telemetry from an application and set log-based alerts to trigger when certain patterns are detected.

    Remember that the above pulumi.export lines will show the IDs of the created resources in the Pulumi output, which can be helpful for debugging and managing these resources through the Azure Portal or CLI.