Monitoring Machine Learning Pipelines with NewRelic AlertChannel

Question

Pulumi · Accepted Answer

To monitor your Machine Learning (ML) pipelines using New Relic, you will need to first set up proper alerting mechanisms to be notified of any issues that might arise. New Relic offers an extensive suite of tools for monitoring applications, and one of these tools is the AlertChannel. An AlertChannel in New Relic acts as a means of communication where you can send notifications about your ML pipeline's performance or any problems that may occur.

Here's how you can use Pulumi with the New Relic provider to set up an AlertChannel:

1. **NewRelic AlertChannel:** You will create a New Relic AlertChannel to receive notifications. This can be an Email channel, a Slack channel, a Webhook, or any of the numerous types available on New Relic, depending on how you want to receive the alerts.

2. **NewRelic AlertPolicy:** In conjunction with the AlertChannel, you will need an AlertPolicy, which defines the conditions under which you want to trigger an alert.

3. **NewRelic InfraAlertCondition:** Finally, you will set up an InfraAlertCondition which specifies the particular infrastructure conditions (like high CPU usage, memory leaks, etc.) that you want to monitor and will reference the AlertPolicy you created.

Now, let's construct a simple Pulumi program to set up a monitoring pipeline with an AlertChannel that sends notifications to an email address. Please ensure you have the required New Relic credentials and API keys set up in your Pulumi configuration or environment variables, as this is imperative for the Pulumi New Relic provider to work.

Below is a Pulumi program written in Python that defines an AlertChannel, an AlertPolicy, and an InfraAlertCondition:

```python
import pulumi
import pulumi_newrelic as newrelic

# Create a New Relic Alert Policy
alert_policy = newrelic.AlertPolicy("ml-pipeline-policy",
    name="MachineLearningPipelinePolicy",
    incident_preference="PER_POLICY",
    # The policy's documentation URL: https://www.pulumi.com/registry/packages/newrelic/api-docs/alertpolicy/
)

# Create a New Relic Notification Channel - in this case, an Email channel
email_channel = newrelic.NotificationChannel("ml-pipeline-email-channel",
    name="MLPipelineEmailChannel",
    type="email",
    configuration={
        "recipients": "your-email@example.com",
        "include_json_attachment": "true",
    },
    # The NotificationChannel's documentation URL: https://www.pulumi.com/registry/packages/newrelic/api-docs/notificationchannel/
)

# Associate the Email channel with the Alert Policy
policy_channel = newrelic.AlertChannelPolicy("ml-pipeline-policy-channel",
    channel_id=email_channel.id,
    policy_id=alert_policy.id,
)

# Create an Alert Condition for the New Relic Infrastructure monitoring
alert_condition = newrelic.InfraAlertCondition("high-cpu-usage",
    policy_id=alert_policy.id,
    type="infra_metric",
    name="High CPU Usage",
    event="SystemSample",
    select="cpuPercent",
    comparison="above",
    critical={
        "duration": 5,
        "value": 90,
        "time_function": "all"
    },
    # The InfraAlertCondition's documentation URL: https://www.pulumi.com/registry/packages/newrelic/api-docs/infraalertcondition/
)

# Output the IDs of the resources created
pulumi.export('alert_policy_id', alert_policy.id)
pulumi.export('email_channel_id', email_channel.id)
pulumi.export('alert_condition_id', alert_condition.id)
```

In this program:

- We create an `AlertPolicy` called "MachineLearningPipelinePolicy" that defines how incidents are rolled-up and managed.
- We then create a `NotificationChannel` of type "email" called "MLPipelineEmailChannel" to send out email notifications.
- We associate the `NotificationChannel` with our `AlertPolicy` using an `AlertChannelPolicy`.
- We define an `InfraAlertCondition` that monitors for high CPU usage, in this case, triggering a critical alert if CPU usage goes above 90% for a duration of at least 5 minutes.

This is a straightforward example that just monitors CPU usage, but New Relic can monitor many other ML pipeline metrics, such as memory usage, database response times, error rates, etc. You'd need New Relic agents installed and reporting data for the relevant infrastructure for these alerts to work accurately. The critical thresholds, duration, and other parameters can also be adjusted based on what you need for your particular ML pipeline monitoring.

Remember to replace "your-email@example.com" with the actual email address where you would like to receive the notifications. Any modifications to the conditions or the addition of more monitoring aspects can be managed by adding more instances or changing the properties of `InfraAlertCondition` and associating them with the existing or new policies.

After running this Pulumi program and standing up the resources, New Relic will send an email notification to the specified email address whenever the high CPU usage condition is met.

Don't forget to check the official [Pulumi New Relic provider documentation](https://www.pulumi.com/registry/packages/newrelic/) for more details about each resource and their configurations.