Latency Threshold Notifications for AI Inference Services on GCP

Question

Pulumi · Accepted Answer

In order to set up latency threshold notifications for AI Inference services on GCP with Pulumi, we will leverage several GCP components:

1. **AI Platform Predictions** - This is the service that will be running the AI models and where we'll monitor latency. We would need an existing model hosted on AI Platform Predictions to set up monitoring, but in the example, we will assume that it is already in place since Pulumi does not currently provide a resource for directly managing AI Platform Predictions models.

2. **Cloud Monitoring** - We'll use Cloud Monitoring to observe the latency of the AI inference service. This involves creating a metric that represents the latency of our AI service.

3. **Alert Policy** - Within Cloud Monitoring, we will create an Alert Policy to monitor when the latency of the AI inference service exceeds a certain threshold.

4. **Notification Channels** - Finally, when an alert is fired, we will use a Notification Channel to send the notification to the appropriate destinations such as email, SMS, or another notification service.

Below is a Python program using Pulumi that defines an alert policy and a notification channel within GCP. In reality, we'd also have to define a metric that represents the latency of the AI inference service, but this detail is usually specific to the setup of the AI Platform Predictions and is out of the scope of what Pulumi manages.

```python
import pulumi
import pulumi_gcp as gcp

project = gcp.config.project

# Create a Notification Channel
# (for the sake of example, we are using email here)
email_notification_channel = gcp.monitoring.NotificationChannel("emailNotificationChannel",
    display_name="Email Channel for AI Inference Latency Alerts",
    type="email",
    labels={
        "email_address": "alert-recipient@example.com", # Replace with actual email address
    },
    user_labels={
        "owner": "ai-team",
    },
    enabled=True
)

# Create an Alert Policy
ai_latency_alert_policy = gcp.monitoring.AlertPolicy("aiLatencyAlertPolicy",
    display_name="AI Inference Latency Threshold",
    enabled=True,
    conditions=[{
        "displayName": "Latency Threshold Breached",
        "condition_threshold": {
            "filter": 'metric.type="custom.googleapis.com/inference/latency" AND resource.type="global"', # This filter would be specific to the metric you have created for the service
            "comparison": "COMPARISON_GT",
            "duration": "60s",
            "thresholdValue": 500, # The latency threshold value in milliseconds
            "aggregations": [{
                "alignmentPeriod": "60s",
                "perSeriesAligner": "ALIGN_RATE",
            }],
        },
    }],
    notification_channels=[email_notification_channel.id],
    combiner="OR",
    user_labels={
        "service": "ai_platform_prediction",
    }
)

# Export the IDs of the created resources
pulumi.export('notification_channel_id', email_notification_channel.id)
pulumi.export('alert_policy_id', ai_latency_alert_policy.id)
```

This Pulumi program is written in Python and will create a new email notification channel and an alert policy within GCP. The alert policy is set up to trigger when our custom latency metric exceeds 500ms over a period of 60 seconds.

In the alert policy, you see a `filters` field, which in practice needs to be adjusted to target the specific metric you are monitoring. The example metric filter `'metric.type="custom.googleapis.com/inference/latency" AND resource.type="global"'` is placeholders and should be replaced with the actual metric that represents the latency of your AI service.

The `conditions` list can include multiple conditions that define various criteria for alerting, including specific metrics, threshold values, and durations.

You would need to replace `"email_address": "alert-recipient@example.com"` with the actual email you wish to receive notifications on.

Finally, we export the IDs of the created notification channel and alert policy so they can be referenced elsewhere if needed. This could be in other Pulumi programs or in any tool that integrates with the Pulumi deployments.

Keep in mind there are some placeholders and assumptions in this code that would need to be replaced with your actual GCP project configuration. As your requirements evolve, you can expand this Pulumi code to add more sophisticated alerting and monitoring setups.