1. Monitoring AI Model API Endpoints with New Relic Synthetics.


    To monitor AI Model API endpoints with New Relic Synthetics using Pulumi, we'll declare resources that enable such monitoring. New Relic Synthetics allows you to monitor your websites and APIs by running checks at regular intervals to verify that they are operational.

    First, you'll need to have an account on New Relic and obtain the necessary API keys and account ID to authenticate with New Relic's service.

    We'll use the pulumi_newrelic provider to configure a Synthetics monitor. Here's the basic workflow:

    1. Import the necessary Pulumi and New Relic packages.
    2. Set up a Synthetics Monitor, which represents the API endpoint you want to check. You need to provide the URI of the API, the type of check (SIMPLE or BROWSER, for example), the frequency of checks, and other specifics about the request like headers or request body.
    3. Optionally, set up AlertCondition for the Monitor that you created, which allows you to trigger alerts based on certain conditions like failed checks.

    Below is a basic Pulumi program in Python that sets up a New Relic Synthetics monitor for an AI Model API endpoint. We'll use a Monitor resource to perform a simple uptime check:

    import pulumi import pulumi_newrelic as newrelic # Required New Relic account ID and API key should be provided via configuration # or environment variables new_relic_account_id = 1234567 # Replace with your New Relic Account ID # Define a New Relic Synthetics Monitor monitor = newrelic.SyntheticsMonitor("ai-model-api-monitor", # Name for the Synthetics Monitor name="AI Model API Endpoint Monitor", # The type of the monitor (SIMPLE, BROWSER, SCRIPT_BROWSER, or SCRIPT_API) type="SIMPLE", # The URI to monitor; replace with the endpoint of your AI Model API uri="https://api.your-ai-model.com/predict", # Frequency of the monitoring checks (in minutes) frequency=5, # The locations from which to run the monitor locations=["AWS_US_WEST_1"], # The status of the monitor (MUTED, DISABLED, or ENABLED) status="ENABLED", # Verify SSL certificate verify_ssl=True, # New Relic account ID associated with the monitor account_id=new_relic_account_id, ) # Optionally, create an alert condition for the monitor alert_condition = newrelic.AlertCondition("ai-model-api-alert-condition", # Name for the alert condition name="AI Model API Endpoint Availability", # New Relic policy ID to attach the condition (this should already exist) policy_id=12345, # Replace with your existing policy ID # The ID of the monitor to attach the condition monitor_id=monitor.id, # Specify whether the condition is turned on enabled=True, ) # Export the monitor's ID and status to easily retrieve them later pulumi.export("monitor_id", monitor.id) pulumi.export("monitor_status", monitor.status)

    This code defines a monitor for an API at https://api.your-ai-model.com/predict and checks its availability every 5 minutes from the AWS US West 1 location. You can customize the uri parameter to point to your specific AI Model API endpoint.

    The second resource, AlertCondition, represents an alert condition linked to the monitor. If the monitor detects that the API endpoint is down, the alert condition can trigger notifications or take other defined actions. To use it, you must replace the policy_id with an ID from one of your existing alert policies within New Relic.

    Remember to replace placeholders (like 1234567 for the New Relic account ID, 12345 for the policy ID, and the API endpoint URI) with actual values before running this program.

    To run this Pulumi program, ensure you have Python and Pulumi installed, and then execute pulumi up on your command line in the directory where the program resides. This will prompt Pulumi to create the specified resources in New Relic.