1. Real-time Anomaly Detection in Machine Learning Pipelines with NewRelic


    Real-time anomaly detection in machine learning pipelines is a crucial task for ensuring the reliability and accuracy of your ML models by immediately identifying unusual patterns that could signify issues such as model drift, data quality problems, or security breaches. Using NewRelic for this purpose involves setting up observability and alerting systems that monitor your machine learning pipelines and send alerts when anomalies are detected.

    In the context of a Pulumi program, you might provision infrastructure that can carry out these tasks - for example, by setting up NewRelic alert policies, workflows, and integrating them with your machine learning system. It's important to note that while Pulumi can provision the infrastructure needed for these tasks, the actual logic for anomaly detection and the specifics of monitoring a machine learning system would be set up within NewRelic itself or the machine learning platform you are using.

    Below is a Python program using Pulumi to set up the initial NewRelic infrastructure for anomaly detection in a machine learning pipeline. This program provisions an alert policy and a workflow in NewRelic that could be the backbone of real-time anomaly detection.

    import pulumi import pulumi_newrelic as newrelic # We will set up a NewRelic Alert Policy. # The Alert Policy serves as a container for a set of alert conditions. # It can be associated with one or more notification channels to inform you about incidents. alert_policy = newrelic.AlertPolicy("ml-anomaly-detection-policy", name="ML Anomaly Detection Policy", # You would replace this with your own NewRelic account ID. accountId=1234567, incidentPreference="PER_POLICY", ) # Assuming you have already set up channels (e.g. email, Slack, etc.) you could add them here. # For simplicity, the setup of channels is omitted in this example. # Now let's create a NewRelic Workflow. # The workflow can set up integrations to send notifications. # In this case, notifications might be handlers that react to detected anomalies. workflow = newrelic.Workflow("ml-anomaly-detection-workflow", name="ML Anomaly Detection Workflow", accountId=1234567, destinations=[ # Here we specify destinations for notifications. The details have been simplified. { "type": "SLACK", "channelId": "example-slack-channel-id", }, ], # The issuesFilter selects the types of issues this workflow will handle. issuesFilter={ "name": "Anomaly Issues Filter", "type": "ISSUE", "predicates": [ { "attribute": "conditionName", "operator": "EQUALS", "values": ["Anomaly Detected Condition"], }, ], }, enabled=True, ) # Export the alert policy ID and workflow ID so we can easily find them in the Pulumi Console or CLI pulumi.export("alert_policy_id", alert_policy.id) pulumi.export("workflow_id", workflow.id) # Please note that to use NewRelic provider, you will need to have appropriate NewRelic credentials configured in your environment.

    In this example:

    • We've created a newrelic.AlertPolicy resource, which will allow you to configure the conditions under which an alert should be triggered for your machine learning pipeline.
    • We've also established a newrelic.Workflow, where you can define the destinations for the alerts (such as Slack) and what types of issues this workflow will handle.

    It's essential to replace placeholders like 1234567 with your actual NewRelic account ID, and configure alert conditions and notification channels as necessary for your specific use case. The ISSUE in issuesFilter would be defined in NewRelic according to the anomaly detection rules you wish to apply.

    These resources do not cover the full breadth of setting up real-time anomaly detection but provide the foundational infrastructure on which you could build a monitoring and alerting system tailored to your needs. The actual conditions and logic for anomaly detection would be something you configure within the NewRelic platform based on your ML pipeline's data and behavior.