1. Real-time AI Application Monitoring on New Relic One Dashboard

    Python

    Monitoring an AI application in real-time involves collecting, analyzing, and visualizing various performance metrics to ensure the application is functioning correctly and efficiently. New Relic is a popular observability platform that allows you to monitor and track the performance of your applications. In the context of using Pulumi to manage the infrastructure for setting up real-time AI application monitoring on a New Relic One dashboard, we'll use the newrelic provider in Pulumi to provision the necessary resources.

    Here's how you would use Pulumi with the New Relic provider to create a dashboard for monitoring your AI application:

    1. We'll create a OneDashboard resource, which represents a New Relic One dashboard.
    2. We will define the widgets that will display the metrics we're interested in. Each widget will include NRQL queries — New Relic Query Language queries — that fetch the data we want to display.
    3. The dashboard will also include variables and pages as necessary to organize and filter the displayed data.

    Below is a Python program using Pulumi, which will provision a New Relic One dashboard with hypothetical widgets suitable for monitoring an AI application:

    import pulumi import pulumi_newrelic as newrelic # Create a New Relic One Dashboard for real-time AI application monitoring. dashboard = newrelic.OneDashboard("aiMonitoringDashboard", # The name of the dashboard. name="AI Application Monitoring", # The account ID where the dashboard will be created. accountId=12345678, # Define the pages on the dashboard. pages=[ newrelic.OneDashboardPageArgs( name="Overview", widgets=[ # Add a widget with an NRQL query to monitor response times. newrelic.OneDashboardWidgetBarArgs( title="Response Times", nrqlQueries=[ newrelic.OneDashboardWidgetNrqlQueryArgs( accountId=12345678, query="SELECT average(duration) FROM Transaction TIMESERIES" ) ] ), # Add a widget to monitor error rates. newrelic.OneDashboardWidgetLineArgs( title="Error Rate", nrqlQueries=[ newrelic.OneDashboardWidgetNrqlQueryArgs( accountId=12345678, query="SELECT rate(count(*), 1 minute) FROM TransactionError TIMESERIES" ) ] ), # Add more widgets as needed... ], description="Page showing an overview of AI application metrics.", ), # Add more pages as needed... ], description="A dashboard to monitor AI applications in real-time." ) # Export the dashboard URL so it can be easily accessed. pulumi.export("dashboard_url", dashboard.url)

    In the above code:

    • We import the pulumi and pulumi_newrelic modules, giving us access to the classes needed to create a New Relic dashboard.
    • We instantiate a OneDashboard resource with a name, account ID, pages, and widgets.
    • Each widget takes an nrqlQueries argument, which is a list of NRQL query statements that fetch and display the real-time metrics on the dashboard.
    • Finally, we export the URL of the dashboard for easy access once the Pulumi program is deployed.

    You would replace the account ID and NRQL queries with values appropriate for your particular AI application and the metrics you want to monitor.

    Please ensure you have the New Relic provider installed and configured with the necessary credentials before running this Pulumi program. To run the program, you would save the code into a file (e.g., monitoring.py), navigate to the directory containing the file in your terminal, and execute pulumi up to preview and deploy the changes.

    For more information on creating dashboards with New Relic and Pulumi, you can check out the following documentation:


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