1. Centralized Notifications for GCP AI Workload Anomalies


    To set up centralized notifications for Google Cloud Platform (GCP) artificial intelligence (AI) workload anomalies, Pulumi can orchestrate the necessary cloud resources to monitor your AI workloads and notify you when anomalies are detected. In this context, an "anomaly" might be an unusual spike in resource usage, failure in the AI service, or an unexpected pattern in data processing.

    We'll use the following GCP services for this purpose:

    1. Google Cloud Monitoring (Stackdriver): We will create an Alert Policy that watches metrics or events that can indicate anomalies in AI workloads.
    2. Notification Channels: These are used to send notifications when an Alert Policy is triggered. Examples include email, SMS, or integration with third-party services like Slack or PagerDuty.
    3. Google Cloud Pub/Sub: This messaging service can also be used as a conduit for notifications, where a Pub/Sub topic can be set up as a Notification Channel.

    In the Pulumi program below, we'll set up an Alert Policy to monitor a dummy metric that stands in for your AI workload anomalies and a Notification Channel that sends notifications to an email address.

    Remember to replace the dummy values with actual metrics that your AI workload emits, and configure the email notification channel with your email information.

    import pulumi import pulumi_gcp as gcp # Set up a Monitoring Notification Channel to receive alerts via email. email_notification_channel = gcp.monitoring.NotificationChannel("email-notification-channel", display_name="Email Notification Channel", type="email", labels={ "email_address": "alerting@example.com" # Change to your desired email address }, # More configuration can be added depending on the notification service ) # Check GCP Monitoring Alert Policy documentation for configuring thresholds and conditions. # https://www.pulumi.com/registry/packages/gcp/api-docs/monitoring/alertpolicy/ alert_policy = gcp.monitoring.AlertPolicy("ai-workload-alert-policy", display_name="AI Workload Anomaly Alert Policy", combiner="OR", # How to combine multiple conditions: AND or OR conditions=[{ # Your specific conditions go here. The following is an example. "displayName": "AI Workload Exceeded Threshold", "conditionThreshold": { "filter": "metric.type=\"custom.googleapis.com/ai/workload/cpu/utilization\"", "comparison": "COMPARISON_GT", "thresholdValue": 0.9, # 90% CPU utilization "duration": "300s", # Lasting longer than 5 minutes "aggregations": [{ "alignmentPeriod": "60s", "perSeriesAligner": "ALIGN_RATE", }], }, }], notification_channels=[email_notification_channel.id] ) pulumi.export("notification_channel_id", email_notification_channel.id) pulumi.export("alert_policy_id", alert_policy.id)

    In this program, email_notification_channel is set up to send alerts to a specified email address when an alert policy is triggered. Adjust the filter and conditions in the alert_policy variable to match the specific metrics or log-based criteria relevant for monitoring your AI workloads. For example, 'custom.googleapis.com/ai/workload/cpu/utilization' should be replaced with the appropriate metric from your AI workload.

    After running this Pulumi program, it will output the IDs for the notification channel and alert policy, which you can use to reference these resources or to link them with other GCP resources. You can further customize the alert conditions and notification options based on the comprehensive features offered by GCP.