1. Anomaly Detection in AI Workloads with GCP Logging Metrics


    Anomaly detection in AI workloads is a critical aspect of monitoring machine learning systems. By tracking and analyzing your model's operational metrics, you can identify unusual patterns that may indicate underlying issues, such as data drift, model degradation, or even external factors affecting model performance.

    In Google Cloud Platform (GCP), you can employ Logging Metrics to capture logs that reflect your AI model's behavior. This includes custom metrics that you define, based on log data that you specify using filters. Once your logs are captured as metrics, you can then use these metrics to identify anomalies in your AI workloads. For example, you might use these metrics to trigger alerts when inference latency exceeds acceptable thresholds, or when model accuracy drops below a certain percentage.

    To set up anomaly detection with GCP Logging Metrics, let’s implement this with Pulumi. We'll create a custom log-based metric that tracks errors in AI predictions. You can adjust this implementation to monitor other aspects of your workloads as needed.

    Here’s a step-by-step guide, followed by a Pulumi Python program that creates a custom log-based metric for anomaly detection:

    1. Define a Log Metric: We start by defining a Metric resource. This metric filters the logs to capture specific events, such as error logs generated by your AI workload.

    2. Set Metric Details: We need to specify the metric details, such as the filter for the log entries and the description of what the metric tracks. The filter is written using the Google Cloud Logging query language.

    3. Using the Metric: After the metric is created, it can be used to create alert policies or dashboards within Google's Cloud Monitoring service.

    Below is the Pulumi program that creates a custom log-based metric:

    import pulumi import pulumi_gcp as gcp # Define a custom log-based metric for anomaly detection ai_workload_metric = gcp.logging.Metric("ai_workload_metric", filter="severity=ERROR AND resource.type=k8s_container AND resource.labels.container_name=ai-predict", metric_descriptor=gcp.logging.MetricMetricDescriptorArgs( metric_kind="DELTA", value_type="INT64", display_name="AI Workload Anomaly Metric", labels=[ gcp.logging.MetricMetricDescriptorLabelArgs( key="job", value_type="STRING", description="The specific job that reported the anomaly" ) ] ), description="This metric counts the number of error logs from the AI prediction jobs.") # (Optional) Export the metric's id for reference and use in other resources, such as alerting policies. pulumi.export('ai_workload_metric_id', ai_workload_metric.id)

    What does the program do?

    • We import the required Pulumi modules.
    • We create a new Logging Metric named ai_workload_metric.
    • The metric filter looks for logs where the severity level is ERROR and originates from a Kubernetes container running an AI workload.
    • We define the kind of metric as DELTA (which signifies a change over time) and of type INT64 (which is a numeric value).
    • We add a label descriptor to provide additional metadata about the logs which is useful for filtering and aggregation in queries and charts.
    • Finally, we export the metric ID so it can be referenced in alerting or monitoring configurations.

    Next Steps

    • The above script only sets up the metric. To fully establish anomaly detection, you would need to create monitoring and alerting policies using this metric.
    • Integrate this setup into a broader observability framework by considering logging, tracing, and monitoring best practices.

    For more information about the log-based metrics in GCP, you can refer to the GCP Logging Metrics documentation.