Endpoint Uptime Assurance for Machine Learning APIs with StatusCake

Question

Pulumi · Accepted Answer

To provide uptime assurance for your Machine Learning API endpoints, we can utilize the `statuscake.UptimeCheck` resource from the StatusCake provider. This resource allows you to create uptime checks which will regularly send HTTP or TCP requests to your API endpoints to make sure they're responding correctly.

When an endpoint goes down or starts responding with unexpected status codes, StatusCake can alert you via email, SMS, or various integrations like Slack, Discord, etc. This way, you can be informed of any downtime as soon as it occurs and take immediate action to resolve the issue, thus assuring the uptime of your Machine Learning APIs.

Below is a Pulumi program that demonstrates how to set up an uptime check using StatusCake in Python. We'll set up a basic HTTP check for the purpose of this example:

```python
import pulumi
import pulumi_statuscake as statuscake

# Create a contact group to be alerted when your API goes down.
contact_group = statuscake.ContactGroup(
    "api-team-contact",
    email_addresses=["api-team@example.com"],
    # Link to documentation: https://www.pulumi.com/registry/packages/statuscake/api-docs/contactgroup/
)

# Define an HTTP check for your machine learning API endpoint.
# You'll need to replace 'your_api_endpoint_url' with the actual URL of your ML API.
api_uptime_check = statuscake.UptimeCheck(
    "ml-api-uptime-check",
    website_url="your_api_endpoint_url",
    check_rate=300,  # Check every 5 minutes (value in seconds).
    test_type="HTTP",
    contact_groups=[contact_group.name],
    # The following line defines expected status codes for a healthy response.
    # You might want to adjust these based on the specifics of your API.
    status_codes="204,205,206,301,302,307,308,400,401,403,404,405,406,408,409,410,411,412,413,414,415,416,417,418,422,425,426,428,429,431,451,500,501,502,503,504,505,506,507,508,510,511",
    # Link to documentation: https://www.pulumi.com/registry/packages/statuscake/api-docs/uptimecheck/
)

# Export the details of the uptime check to be accessible outside of Pulumi.
pulumi.export("uptime_check_id", api_uptime_check.id)
```

In this program, we first create a contact group. This group represents the list of contacts that will receive notifications when your API's uptime check fails. Feel free to add email addresses or other notification methods supported by StatusCake.

Next, we define an `UptimeCheck`. The `website_url` should be the endpoint of your Machine Learning API that you want to monitor. The `check_rate` is how often StatusCake will perform the uptime check, set here to every 5 minutes (300 seconds). The `test_type` specifies that we’re doing an HTTP check. The `contact_groups` argument links our `UptimeCheck` to the previously created contact group, so that the specified contacts are alerted on status changes.

Lastly, we're exporting the `id` of the uptime check, allowing you to identify this resource within the StatusCake dashboard or API for later reference or modifications.

Please replace `'your_api_endpoint_url'` with the actual endpoint of your Machine Learning API that you're looking to monitor.

With these steps, you'll be able to ensure that your Machine Learning API's availability is monitored and that you're quickly informed about any potential downtime.