Automated Anomaly Detection in AI Systems with Datadog
PythonTo 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
anddatadog.MetricMetadata
. Thedatadog.Monitor
resource allows you to create and manage Datadog monitor configurations, which can be used to detect anomalies based on various metrics. Thedatadog.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:
-
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
. Thequery
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 asmessage
to notify our team when an anomaly is detected. -
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 theanomalies
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 theai.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.
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