Logging and Error Detection in AI Applications with GCP Monitoring

Question

Pulumi · Accepted Answer

When it comes to setting up logging and error detection for AI applications on Google Cloud Platform (GCP), you can benefit from the GCP Monitoring and Logging services. These services allow you to track application performance, log events, and set alerts for specific error conditions.

Here's a breakdown of how you could set this up with Pulumi in Python:

1. **Monitoring Metrics and Dashboards**: You use the `gcp.monitoring.MetricDescriptor` resource to define custom metrics for your application. Then, you can visualize these metrics by creating a dashboard with the `gcp.monitoring.Dashboard` resource.

2. **Logging**: You can configure your application to output logs to `stdout` and `stderr`, which GCP automatically captures and sends to the Stackdriver Logging service. Use the `gcp.monitoring.Group` resource to manage logs by grouping resources (like AI service instances) whose logs you want to monitor together.

3. **Error Reporting**: To enable error reporting, ensure that your application logs are formatted in a way that Stackdriver Error Reporting can recognize. This typically includes exception stack traces. Error Reporting is automatically enabled for many GCP services.

4. **Uptime Checks and Alert Policies**: With `gcp.monitoring.UptimeCheckConfig`, you can set up regular checks to ensure your AI applications are accessible. And with `gcp.monitoring.AlertPolicy`, you can define alerting policies based on metrics or events, such as error rates exceeding a certain threshold.

Now, let's put this together in a Pulumi program:

```python
import pulumi
import pulumi_gcp as gcp

# Configure a custom metric for tracking AI application errors.
ai_error_metric = gcp.monitoring.MetricDescriptor("aiErrorMetric",
    type="custom.googleapis.com/ai_application/errors",
    metric_kind="GAUGE",
    value_type="INT64",
    description="The number of errors encountered by the AI application.",
    display_name="AI Application Errors")

# Set up a dashboard to visualize the custom error metric.
dashboard_json = """{
  "widgets": [
    {
      "title": "AI Application Errors Over Time",
      "xyChart": {
        "dataSets": [
          {
            "timeSeriesQuery": {
              "timeSeriesFilter": {
                "filter": "metric.type=\"custom.googleapis.com/ai_application/errors\""
              }
            }
          }
        ],
        "timeshiftDuration": "0s",
        "chartOptions": {
          "mode": "COLOR"
        }
      }
    }
  ]
}"""

ai_errors_dashboard = gcp.monitoring.Dashboard("aiErrorsDashboard",
    dashboard_json=dashboard_json)

# Define an alert policy for when the error count exceeds a certain threshold.
error_count_threshold = gcp.monitoring.AlertPolicy("errorCountThreshold",
    combiner="OR",
    conditions=[{
        "displayName": "Error rate high",
        "condition_threshold": {
            "filter": "metric.type="custom.googleapis.com/ai_application/errors"",
            "comparison": "COMPARISON_GT",
            "thresholdValue": 100,
            "duration": "600s",
            "aggregations": [{
                "alignment_period": "60s",
                "perSeriesAligner": "ALIGN_RATE"
            }],
        },
    }],
    display_name="Error Rate High Alert")

# Set up an uptime check for the application's endpoint.
ai_app_uptime_check = gcp.monitoring.UptimeCheckConfig("aiAppUptimeCheck",
    display_name="AI App Uptime Check",
    http_check={
        "path": "/health",
        "port": 80,
    },
    period="60s",
    timeout="10s",
    selected_regions=["USA"])

# Export the URL to access the AI application errors dashboard.
pulumi.export('ai_errors_dashboard_url', ai_errors_dashboard.self_link)
```

In this program:

- We've created a custom metric descriptor for tracking errors in your AI application.
- A dashboard is set up with a widget to visualize the error count over time.
- We've defined an alert policy that will trigger if the error count goes above a certain value (100 in this case) within a 10-minute window.
- An uptime check ensures that the AI application's endpoint is accessible from different regions at regular intervals.
- The URL to the dashboard can be accessed via the stack output for easy reference.

Remember to have your AI application push relevant metrics to the custom-defined metrics and logging. You also need to handle the alerts generated by the monitoring system and set up proper error logging in your application to make the most out of GCP’s monitoring and error reporting features.