Anomaly Detection for Machine Learning Workloads with GCP Monitoring

Question

Pulumi · Accepted Answer

Anomaly detection in machine learning workloads often involves monitoring various metrics to identify unusual patterns that could indicate a problem. Google Cloud Platform (GCP) offers several services that can be used to monitor these metrics and provide alerting mechanisms when anomalies are detected.

Below, I'll guide you through setting up a solution on GCP using Pulumi, that includes the following:

Monitoring Dashboard: A dashboard to visualize the metrics of interest.
Monitoring Groups: Groups to organize and manage associated resources.
Monitoring MetricDescriptor: To create custom metrics specific for machine learning workloads.
Monitoring AlertPolicy: To configure alerts based on thresholds related to the anomaly detection logic.

Let's write the Pulumi program in Python to set this up:

import pulumi
import pulumi_gcp as gcp

# Replace 'your_project' with your GCP project ID.
project = 'your_project'

# Create a Monitoring Dashboard for visualizing anomaly metrics.
dashboard = gcp.monitoring.Dashboard("anomaly-dashboard",
    # In a real-world scenario, you would define the configuration of your monitoring dashboard here.
    # This typically includes the specification of what metrics to plot, and in what format (graphs, charts, tables etc.).
    dashboard_json="<YOUR_DASHBOARD_JSON_CONFIGURATION>",
    project=project
)

# Define a Monitoring Group for the machine learning workloads.
group = gcp.monitoring.Group("ml-group",
    display_name="Machine Learning Workloads",
    filter="<RESOURCE_FILTER>",  # Specify filter to match resources to include in the group.
    project=project
)

# Define custom Metric Descriptor.
# This metric descriptor defines the structure of the custom metric you will use to represent your anomaly detection metric.
metric_descriptor = gcp.monitoring.MetricDescriptor("anomaly-metric-descriptor",
    description="Custom metric for anomaly detection",
    display_name="AnomalyDetectionMetric",
    type="custom.googleapis.com/ml_anomaly_score",  # Custom metric type format for GCP.
    metric_kind="GAUGE",  # Metric type could be GAUGE, DELTA, or CUMULATIVE. This depends on how your score is reported.
    value_type="DOUBLE",  # This metric records a numerical value. You could also use INT64, STRING, BOOL, etc.
    project=project
)

# Define an Alert Policy for anomaly detection.
# This alerting policy triggers if the custom anomaly metrics breach the thresholds you define, suggesting an anomaly in ML workloads.
alert_policy = gcp.monitoring.AlertPolicy("anomaly-alert-policy",
    combiner="OR", # How the conditions are combined (AND means all conditions must be met, OR means any condition can trigger the alert).
    conditions=[{
        "displayName": "Anomaly Detected",
        "conditionThreshold": {
            "filter": f'metric.type="{metric_descriptor.type}"',
            "duration": "300s",  # The duration for which the threshold must be continuously breached to trigger the alert.
            "comparison": "GREATER_THAN",  # How to compare the current metric value with the threshold.
            "thresholdValue": 0.9,  # Threshold value to trigger the alert.
            "aggregations": [{
                # The aggregation method used to combine time series.
                "alignmentPeriod": "300s",
                "perSeriesAligner": "ALIGN_MAX",  # ALIGN_MAX is used here for demonstration. The aligner should match your anomaly logic.
            }],
        },
    }],
    project=project
)
pulumi.export('dashboard_url', dashboard.self_link)
pulumi.export('group_id', group.id)
pulumi.export('metric_descriptor_name', metric_descriptor.name)
pulumi.export('alert_policy_name', alert_policy.name)

Let's break down what this code does:

Dashboard: We create a monitoring dashboard designed to visualize the metrics we're interested in for our machine learning workloads. This dashboard is defined by a JSON configuration that specifies the layout and the metrics to display.
Monitoring Group: We set up a group for organizing our machine learning resources. These could be virtual machine instances or container instances where machine learning models are deployed.
Metric Descriptor: Since we might have custom metrics specific to our workload (for example, the 'anomaly score'), we define a custom metric descriptor.
Alert Policy: We create an alert policy that triggers when the values of our anomaly detection metric (defined by the custom metric descriptor) breach a certain threshold. This could indicate a potential anomaly in our machine learning workloads.

After deploying this configuration with Pulumi, you would get a fully set up monitoring system on GCP that helps you keep track of your machine learning workloads and alerts you to potential issues. Make sure to replace placeholders like "<YOUR_DASHBOARD_JSON_CONFIGURATION>" and "<RESOURCE_FILTER>" with actual values for your setup.

To get more detailed information on how to configure each of these resources, you can refer to the respective documentation for Dashboard, Group, Metric Descriptor, and Alert Policy.