1. Automated Anomaly Detection in AI Systems with Datadog


    To set up automated anomaly detection in AI systems with Datadog using Pulumi, we will use two primary resources from the Datadog Pulumi provider: datadog.Monitor and datadog.MetricMetadata. The datadog.Monitor resource allows you to create and manage Datadog monitor configurations, which can be used to detect anomalies based on various metrics. The datadog.MetricMetadata resource is used to assign metadata to custom metrics, helping with organization and interpretation.

    Here's a detailed explanation of how we can create a Datadog-powered anomaly detection system:

    1. datadog.Monitor: This is the core resource to set up anomaly detection. We can define the type of monitor we want, such as query alert. The query property specifies what metric we want to monitor. Datadog provides an anomaly detection function that we can use within this query to detect unexpected behavior. We will also set properties such as message to notify our team when an anomaly is detected.

    2. datadog.MetricMetadata: This is an optional step if you have custom metrics and want to set or update their metadata to help with clarity and filtering within the Datadog UI.

    First, we will establish the monitor for anomaly detection. In our case, let's say that our AI system reports a metric called ai.system.inference.time which records the time taken for an inference. We want to monitor this metric for any unusual spikes or drops which could indicate potential problems.

    To get started with Pulumi, you would first need to install the Pulumi CLI and the Datadog Pulumi provider. Afterward, you could use the Pulumi Python SDK to write your program like below:

    import pulumi import pulumi_datadog as datadog # Define the monitor for anomaly detection anomaly_detection_monitor = datadog.Monitor("anomalyDetectionMonitor", type="query alert", query="avg(last_5m):anomalies(avg:ai.system.inference.time{environment:production}.fill(null), 'basic', 2)", name="AI System Inference Time Anomaly Detection", message="Notification Message @pagerduty", tags=["ai-system", "anomaly-detection", "production"], priority=1 ) # Optionally define metric metadata if you have custom metrics metric_metadata = datadog.MetricMetadata("aiSystemInferenceTimeMetadata", metric="ai.system.inference.time", type="gauge", description="Time taken for the AI system to perform an inference", shortName="Inference Time", unit="seconds", perUnit="inference" ) # Export the anomaly detection monitor id pulumi.export("anomalyDetectionMonitorId", anomaly_detection_monitor.id)

    In the above program,

    • We create a monitor with type="query alert" which is suitable for anomaly detection.
    • The query uses the anomalies function provided by Datadog to analyze your specified metric over a certain period (last_5m refer to the last 5 minutes here) and to identify any behavior that is outside of what's expected (basic algorithm with two deviations).
    • We define a message that includes an alert notification system (in this case @pagerduty) to be notified when an anomaly is detected.
    • The datadog.Monitor resource is tagged with relevant labels like "anomaly-detection" and "production" which helps with organizing and filtering monitors within Datadog.
    • We create a datadog.MetricMetadata resource to add additional context for the ai.system.inference.time metric.

    This program is a starting point and can be expanded upon depending on the complexity and specifics of your AI system and the metrics you want to monitor.

    You can learn more about Datadog monitors in Pulumi from Datadog Monitor and for metric metadata from Datadog MetricMetadata.