Monitoring AI Model API Endpoints with New Relic Synthetics.

Question

Pulumi · Accepted Answer

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:

```python
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.