PagerDuty for Real-Time Anomaly Detection Alerts in ML Ops

Question

Pulumi · Accepted Answer

To integrate PagerDuty with a Machine Learning Operations (ML Ops) application for real-time anomaly detection alerts, you will first need a PagerDuty service set up to handle incoming alerts, and an integration to connect your alerting system with PagerDuty. Below, I'm going to provide you a Pulumi program that sets up a PagerDuty service, an escalation policy to determine the alert notification order, and a service integration that allows alerts from your ML Ops system (which we'll assume can send alerts via a generic API) to be routed to PagerDuty.

First, we will import necessary modules:
- `pulumi` for general Pulumi resources,
- `pulumi_pagerduty` to interact with the PagerDuty resources.

We will create the following resources:
- `pagerduty.EscalationPolicy`: Defines the order in which users or schedules are notified about an incident.
- `pagerduty.Service`: Represents a service that you are monitoring (in this case, your ML Ops application).
- `pagerduty.ServiceIntegration`: Defines an integration between the PagerDuty service and your ML Ops application.

Here's a breakdown of each step with the corresponding Pulumi Python code.

```python
import pulumi
import pulumi_pagerduty as pagerduty

# Create a new escalation policy for alerts.
# This policy determines which users get notified at different stages of an incident.
escalation_policy = pagerduty.EscalationPolicy("ml-ops-escalation-policy",
    description="Escalation policy for ML Ops alerts",
    teams=[],  # Specify PagerDuty Teams if needed
    rules=[pagerduty.EscalationPolicyRuleArgs(
        escalation_delay_in_minutes=30,
        targets=[pagerduty.EscalationPolicyRuleTargetArgs(
            # Replace `user_id` with the actual user ID you want to notify.
            id="USER_ID",
            type="user",
        )]
    )]
)

# Create a new PagerDuty service that represents the ML Ops application.
service = pagerduty.Service("ml-ops-service",
    acknowledgement_timeout="600",  # Change to your own timeout policy
    auto_resolve_timeout="14400",  # Change to your own policy
    escalation_policy=escalation_policy.id,
    incident_urgency_rule=pagerduty.ServiceIncidentUrgencyRuleArgs(
        type="constant",  # Set to "use_support_hours" if you want to define during and outside support hours.
        urgency="high"     # Customize the urgency as needed.
    )
)

# Create a new integration for your ML Ops application with the newly created service.
# This will generate an integration key that your application will use to send alerts.
service_integration = pagerduty.ServiceIntegration("ml-ops-integration",
    service=service.id,
    type="generic_events_api_v2",
    name="ML Ops Anomaly Detection"
)

# Output the integration key that will be used by the ML Ops application to send alerts to PagerDuty.
pulumi.export("integration_key", service_integration.integration_key)
```

With this program, a new PagerDuty service for your ML Ops application is created with an escalation policy to notify the appropriate user(s), and a service integration to connect PagerDuty with your system.

The resulting `integration_key` exported at the end is critical as you'll use this key for your ML Ops system to send alerts to PagerDuty. You would usually configure your anomaly detection system to send alerts with this key as part of the API request to PagerDuty.

Remember, the actual ID of the user to notify (referred to in this code as `USER_ID`) should be replaced with the actual user's ID in PagerDuty. If you have multiple users or schedules, add them as targets in the escalation rule.

Please ensure you have the `pulumi_pagerduty` plugin installed in your Pulumi environment:

```sh
pip install pulumi_pagerduty
```

Deploy this Pulumi program by running the following commands:

```sh
pulumi up
```

After reviewing the plan, confirm the deployment to provision the resources in PagerDuty. Once deployed, use the exported `integration_key` in your ML Ops system for sending alerts.

If your ML Ops system has specific requirements for the alert format or the integration method, you may need to adjust the `type` parameter of the `pagerduty.ServiceIntegration` resource, or provide additional configuration as needed.