Send Automated Chat Alerts

Send automated alerts to chat services.

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💬 Send Automated Chat Alerts

Many developer teams use real-time chat services like Slack for their day-to-day communication. Members of those teams might also want to have automated notifications be sent to those chat services from within ML pipelines, e.g., to quickly and conveniently get notified about the state of each pipeline, or even to build human-in-the-loop ML systems.

Overview

The alerter component in ZenML allows teams to easily interact with their pipelines via a chat service.

Each alerter integration comes with specific standard steps that you can use out-of-the-box. As an example, the slack integration contains the following two steps:

• slack_alerter_post_step takes a string, posts it to Slack, and returns True if the operation succeeded, else False.

• slack_alerter_ask_step does the same as slack_alerter_post_step, but after sending the message, it waits until someone approves or rejects the operation from within Slack (e.g., by sending "approve" / "reject" to the bot in response). slack_alerter_ask_step then only returns True if the operation succeeded and was approved, else False.

Currently, the SlackAlerter is the only available alerter integration, but it is straightforward to extend ZenML and build an alerter for other chat services, as shown here.

Send alerts in your pipelines

To send alerts in your pipelines, you first need to register an alerter component using zenml alerter register <MY_ALERTER> and then add it to your stack with zenml stack register ... -al <MY_ALERTER>.

Afterward, you can simply import the integrations alerter steps and use them in your pipeline.

Since these steps expect a string message as input (which needs to be the output of another step), you typically also need to define a dedicated formatter step that takes whatever data you want to communicate and generates the string message that the alerter should post.

As an example, adding slack_alerter_ask_step() into your pipeline could look like this:

from zenml.integrations.slack.steps.slack_alerter_ask_step import slack_alerter_ask_step
from zenml.steps import step
from zenml.pipelines import pipeline


@step
def my_formatter_step(artifact_to_be_communicated) -> str:
    return f"Here is my artifact {artifact_to_be_communicated}!"


@pipeline
def my_pipeline(..., formatter, alerter):
    ...
    artifact_to_be_communicated = ...
    message = formatter(artifact_to_be_communicated)
    approved = alerter(message)
    ... # Potentially have different behavior in subsequent steps if `approved`


my_pipeline(
    ...
    formatter=my_formatter_step(),
    alerter=slack_alerter_ask_step(),
).run()

For complete code examples of both Slack alerter steps, see the slack_alert example, where we first send the test accuracy of a model to Slack and then wait with model deployment until a user approves it in Slack.