1. ML Pipelines Anomaly Detection through Datadog for AWS


    To implement a machine learning (ML) pipeline's anomaly detection in AWS with monitoring through Datadog, you will need to set up various AWS resources for the ML pipeline and Datadog resources for observability. Here, I will focus on the Datadog part, specifically setting up a monitor that can detect anomalies in your pipeline's metrics.

    In Pulumi, we can create Datadog resources, such as metrics and monitors, using the pulumi_datadog package. For our use case, you'll want to create a Monitor that will watch a specific metric—perhaps the execution time of your ML pipeline or the error rates—and alert you when it detects values that deviate from the expected range—an anomaly.

    Below is a Pulumi program written in Python that sets up an anomaly detection monitor with Datadog. Remember that you should already have your AWS resources in place for the ML pipeline and your Datadog provider configured with the necessary API and application keys.

    import pulumi import pulumi_datadog as datadog # Define the name of the metric you want to monitor (e.g., 'ml.pipeline.execution.time'). metric_name = "ml.pipeline.execution.time" # Define the anomaly detection query for the monitor. Adjust the query as needed for your metric. anomaly_detection_query = f"anomalies(avg:{metric_name}{{*}}, 'basic', 2)" # Create a Datadog monitor for anomaly detection in the ML pipeline. ml_pipeline_monitor = datadog.Monitor("mlPipelineAnomalyDetectionMonitor", name="ML Pipeline Anomaly Detection", type="query alert", query=anomaly_detection_query, message="Anomaly detected in ML pipeline!", tags=["environment:production", "team:data-science"], priority=3, notify_no_data=True, no_data_timeframe=20, # The timeframe (in minutes) to alert when no data is received. renotify_interval=10, # Time (in minutes) to wait before sending a renotification. options=datadog.MonitorOptionsArgs( evaluate_low_sample_count_percentiles="", new_host_delay=300, include_tags=True, escalate_message="Anomaly persists in ML pipeline!", ) ) # Export the URL to the Datadog dashboard for this monitor. pulumi.export("datadog_dashboard_url", pulumi.Output.concat("https://app.datadoghq.com/monitors/", ml_pipeline_monitor.id))

    In this program, we define the ml_pipeline_monitor which uses the Monitor class from the Pulumi Datadog package. When you create a monitor, you provide several parameters such as the name for identification, the type which here is query alert for alerts based on a query, query which defines the metric to monitor and the condition for anomaly detection, and message to convey what the alert is about when triggered.

    We also include tags to categorize and filter in the Datadog dashboard and priority to indicate the urgency of the monitor. In the options, we can specify additional settings like evaluate_low_sample_count_percentiles, new_host_delay, include_tags, and an escalate_message which is a follow-up message if the condition persists.

    After running the above program with pulumi up, Pulumi will provision a new Monitor in Datadog to detect anomalies as per your defined metric and settings. When an anomaly is detected, you will receive an alert based on the notification settings you have configured in your Datadog account.