Integrating Large Language Models Monitoring with Azure Alerts

Question

Pulumi · Accepted Answer

To integrate Large Language Models monitoring with Azure Alerts, you typically need to set up Azure Monitor alerts that trigger in response to specific metrics or logs that indicate the status or health of your language model service. The Azure Monitor service can collect and analyze telemetry data from various Azure resources, enabling you to detect and respond to issues in real-time.

Below is a step-by-step program in Python using Pulumi to create Azure Monitor Alert Rules. These alert rules can monitor the performance and availability of your Large Language Model application and trigger alerts when certain conditions are met.

1. **Azure Activity Log Alert**: Monitors Azure subscription-level events (such as service health incidents) and can trigger actions when these events occur. Useful for monitoring overall Azure service health that may affect your language models.

2. **Azure Metric Alert**: Monitors platform-level metrics for Azure services, which allows you to trigger alerts based on performance metrics (e.g., CPU usage, memory pressure) of your large language model hosting environment, such as Azure Kubernetes Service (AKS) or Azure Machine Learning service.

3. **Azure Action Group**: Specifies a collection of actions to perform when an alert triggers. For example, you could send an email, trigger an Azure Function, or call a webhook, which could be a service that scales your model up or down.

Please ensure that you have Azure CLI and Pulumi CLI set up on your machine and are logged in to the Azure account before running this Pulumi program.

Here's a Pulumi Python program that sets up an Azure Monitor Metric Alert and Activity Log Alert:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Action Group for receiving alerts
action_group = azure_native.insights.ActionGroup(
    "action-group",
    resource_group_name="my-resource-group",
    short_name="myshortname",
    group_short_name="myactiongroup",
    enabled=True,
    email_receivers=[
        azure_native.insights.EmailReceiverArgs(
            name="send_to_admins",
            email_address="admin@example.com",
            use_common_alert_schema=True,
        )
    ]
)

# Create an Alert Rule for metric-based monitoring (e.g., CPU usage)
metric_alert = azure_native.insights.MetricAlert(
    "cpu-usage-alert",
    resource_group_name="my-resource-group",
    description="Alert on high CPU usage",
    severity=3,
    enabled=True,
    scopes=[
        # Replace with the ID of the resource you want to monitor
        "/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.Compute/virtualMachines/{vm-name}",  
    ],
    evaluation_frequency="PT1M",  # Evaluate every minute
    window_size="PT5M",  # 5 minutes window
    criteria=azure_native.insights.MetricAlertCriteriaArgs(
        metric_name="Percentage CPU",
        metric_namespace="Microsoft.Compute/virtualMachines",
        operator="GreaterThan",
        threshold=80,  # Alert if CPU is greater than 80%
        time_aggregation="Average"
    ),
    actions=[
        azure_native.insights.MetricAlertActionArgs(
            action_group_id=action_group.id,
        )
    ]
)

# Create an Alert Rule for monitoring Azure activity logs
activity_log_alert = azure_native.insights.ActivityLogAlert(
    "service-health-alert",
    resource_group_name="my-resource-group",
    scopes=[
        # Use the subscription you're monitoring
        "/subscriptions/{subscription-id}",
    ],
    actions=azure_native.insights.ActivityLogAlertActionListArgs(
        action_groups=[
            azure_native.insights.ActivityLogAlertActionGroupArgs(
                action_group_id=action_group.id
            )
        ]
    ),
    all_of_conditions=[
        azure_native.insights.ActivityLogAlertAllOfConditionArgs(
            equals="Administrative",
            field="category"
        ),
        # Add more conditions as needed
    ]
)

# Export the ID of the action group and alert rules
pulumi.export("action_group_id", action_group.id)
pulumi.export("metric_alert_id", metric_alert.id)
pulumi.export("activity_log_alert_id", activity_log_alert.id)
```
- Replace `"my-resource-group"` with the actual Azure resource group name you are using.
- The `scopes` in `metric_alert` and `activity_log_alert` should contain Azure Resource IDs for the resources you want to monitor. Modify these placeholders to match the resources specific to your application.
- In the `metric_alert`, replace placeholders like `{subscription-id}`, `{resource-group}`, and `{vm-name}` with actual values relevant to your Azure environment.
- Modify the email address `"admin@example.com"` under `email_receivers` to an email address where alerts should be sent.

This program sets up both a metric alert and an activity log alert. The metric alert monitors if the CPU usage goes above 80% and will trigger the action group to send out an email. The activity log alert looks for a specific category of logs (in this case, 'Administrative') and also triggers the action group.

After deploying this Pulumi program, if the conditions specified in the alert rules are met, actions defined in the `action_group` will get triggered, such as sending an email notification to specified recipients. You can customize the conditions, metrics, and actions to specifically match the telemetry for your Large Language Models' health and performance indicators.