Synthetic Monitoring for ML Pipelines with Checkly

Question

Pulumi · Accepted Answer

Synthetic monitoring is a technique where automated checks are run against your applications and services to simulate user behavior or API usage. This is usually done to ensure they are functioning as expected and to catch issues before real users encounter them. For implementing synthetic monitoring for Machine Learning (ML) pipelines, we can use third-party services such as Checkly, which offer API and browser check capabilities.

With Pulumi, we can define infrastructure as code, including setting up synthetic monitoring checks with Checkly. This means you can codify the monitoring of your ML pipelines alongside the rest of your cloud infrastructure.

Checkly offers a Pulumi provider, which allows you to create, manage and deploy Checkly checks and check groups through Pulumi. Below I will guide you through setting up some Checkly checks for your ML pipeline using Pulumi.

Assuming you've got an ML pipeline with some HTTP endpoints or services that you want to monitor, here is how you could set this up in Pulumi using Python:

1. **Checkly Check**: A Check is a single URL or API endpoint that you want to monitor.
2. **Checkly Check Group**: A Check Group is a grouping of multiple Checks that can be used to organize and manage related checks together.

The following Pulumi Python program sets up a Checkly Check within a Check Group to monitor an ML pipeline:

```python
import pulumi
import pulumi_checkly as checkly

# This script assumes that you've configured Pulumi for Checkly
# and that you've set the appropriate environment variables or Pulumi configuration for authentication.

# A Checkly Check Group groups together multiple checks.
# Here we create a new check group for our ML pipeline checks.
check_group = checkly.CheckGroup(
    "ml-pipeline-group",
    activated=True,
    muted=False,  # When 'False', notifications will not be sent.
    concurrency=1,  # Number of checks that are allowed to run concurrently within the group.
    locations=["eu-west-1"],  # The regions from which the checks will be run.
    api_check_defaults=checkly.CheckGroupApiCheckDefaultsArgs(
        # Default request headers for the API checks in this group.
        headers={"Content-Type": "application/json"},
    ),
)

# Define a synthetic monitoring check on a hypothetical ML model's health endpoint.
# This check is for monitoring the status of an ML service by hitting its health check endpoint.
api_check = checkly.Check(
    "ml-model-health-check",
    name="ML Model Health Check",
    type="API",  # Type of the check. It could be 'API' or 'BROWSER'.
    request=checkly.CheckRequestArgs(
        method="GET",
        url="https://your-ml-service.com/health",  # This is the URL of the ML service health endpoint.
    ),
    # Assertions define the expected conditions that must hold true for the check to pass.
    assertions=[
        checkly.CheckAssertionArgs(
            source="STATUS_CODE", 
            comparison="EQUALS", 
            target="200"  # We expect a successful 200 response for a healthy service.
        ),
        # You can add more assertions here, such as response time, or specific JSON body content.
    ],
    frequency=10,  # Frequency of the check execution in minutes.
    deactivated=False,  # When 'False', the check is activated and will be regularly executed.
    group_id=check_group.id,  # Associate the check with the previously defined check group.
)

pulumi.export("health_check_id", api_check.id)
```

This code accomplishes several things:
- It defines a Checkly check group named `ml-pipeline-group`, which will contain our checks.
- It then sets up an API check on a health endpoint of an ML service. This check is to ensure that the service is up and responding as expected.
- It makes use of assertions to your Checkly check to define what conditions must be met for the check to be considered successful, such as receiving a 200 status code from the health endpoint.
- Finally, it exports the ID of the created check so you can refer to it if needed.

Remember, for this code to work, you'll need to replace `https://your-ml-service.com/health` with the actual endpoint you wish to monitor.

To deploy these resources, simply run `pulumi up` in the directory containing this code. Pulumi will reach out to Checkly and create these resources for you, and start running checks at the frequency you specified.