The SlackAlerter enables you to send messages to a dedicated Slack channel directly from within your pipelines.

The slack integration also contains the following two standard 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.

Interacting with Slack from within your pipelines can be very useful in practice:

  • The slack_alerter_post_step allows you to get notified immediately when failures happen (e.g., model performance degradation, data drift, …),

  • The slack_alerter_ask_step allows you to integrate a human-in-the-loop into your pipelines before executing critical steps, such as deploying new models.

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How to use it

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Requirements

Before you can use the SlackAlerter, you first need to install ZenML’s slack integration:

zenml integration install slack -y

See the Integrations page for more details on ZenML integrations and how to install and use them.

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Setting Up a Slack Bot

In order to use the SlackAlerter, you first need to have a Slack workspace set up with a channel that you want your pipelines to post to.

Then, you need to create a Slack App with a bot in your workspace.

Make sure to give your Slack bot chat:write and chat:write.public permissions in the OAuth & Permissions tab under Scopes.

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Registering a Slack Alerter in ZenML

Next, you need to register a slack alerter in ZenML and link it to the bot you just created. You can do this with the following command:

zenml alerter register slack_alerter \
    --flavor=slack \
    --slack_token= \
    --default_slack_channel_id=

Here is where you can find the required parameters:

  • <SLACK_CHANNEL_ID>: Open your desired Slack channel in a browser, and copy out the last part of the URL starting with C.....

  • <SLACK_TOKEN>: This is the Slack token of your bot. You can find it in the Slack app settings under OAuth & Permissions.

After you have registered the slack_alerter, you can add it to your stack like this:

zenml stack register ... -al slack_alerter

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How to Use the Slack Alerter

After you have a SlackAlerter configured in your stack, you can directly import the slack_alerter_post_step and slack_alerter_ask_step steps and use them in your pipelines.

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 alerter 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.

For more information and a full list of configurable attributes of the Slack alerter, check out the API Docs.