1. Anomaly Detection for AI Model Performance with NewRelic NRQLAlertCondition


    Anomaly detection is a crucial part of maintaining the performance of AI models in production. It allows you to identify unusual patterns that do not conform to expected behavior. In the context of cloud applications and infrastructure, this can help in proactively spotting issues before they escalate into major problems.

    For AI models, you would typically monitor the performance metrics such as latency, error rates, or the number of predictions made. When these metrics deviate significantly from the norm, it may indicate an issue with the model performance that requires attention.

    We'll use Pulumi to set up anomaly detection for AI model performance using NewRelic's NRQLAlertCondition resource. This resource allows you to define alert conditions based on NRQL (New Relic Query Language) queries. These queries can analyze different metrics and set thresholds for triggering alerts.

    In this example, we will set up a simple NRQL alert condition that triggers if the error rate of predictions from an AI service goes above 5% for at least 5 minutes.

    Here is a Pulumi program in Python to set up such an alert with NewRelic:

    import pulumi import pulumi_newrelic as newrelic # Configuration for the NewRelic alert policy policy_name = "ai_model_performance_policy" policy = newrelic.AlertPolicy(policy_name, name=policy_name) # NewRelic NRQL that defines the condition to monitor the error rate for the AI model nrql_query = "SELECT percentage(count(*), WHERE result='error') FROM Transaction WHERE appName='AI_Model_Service'" # A NRQL alert condition that monitors the error rate # and triggers if the error rate is higher than 5% for at least 5 consecutive minutes. alert_condition = newrelic.NrqlAlertCondition( "ai_model_error_rate", policy_id=policy.id, name="AI Model Error Rate", nrql=newrelic.NrqlAlertConditionNrqlArgs( query=nrql_query, since_value="5" # Time window in minutes ), type="static", critical=newrelic.NrqlAlertConditionCriticalArgs( threshold=5.0, threshold_duration=300, # Duration, in seconds, to evaluate the threshold threshold_occurrences="AT_LEAST_ONCE" ), # This enables the alert condition enabled=True ) # Export the NewRelic alert condition ID pulumi.export("alert_condition_id", alert_condition.id)


    In the program above:

    • We begin by importing the necessary Pulumi packages.
    • We create a newrelic.AlertPolicy that will contain our alert condition. This policy acts as a container for one or more conditions.
    • The nrql_query variable contains the NRQL query string. This specific query calculates the error rate for transactions associated with an application named 'AI_Model_Service'.
    • The newrelic.NrqlAlertCondition resource defines the condition that NewRelic will monitor. We provide it with the policy ID, a name, and details about what to query and the criteria for triggering the alert.
    • The critical argument specifies the threshold that, when breached, will trigger the alert. In this case, we've set a threshold of 5% over a period of 5 consecutive minutes.
    • Finally, we export the alert condition ID for external reference, possibly for use in other operations or for documentation purposes.

    For this to work, you would need to have an AI model service instrumented with NewRelic, where 'AI_Model_Service' is the name of your service within NewRelic.

    By implementing this kind of anomaly detection, you can be quickly alerted to potential issues with your AI model's performance, allowing you to take corrective action swiftly.