Celonis Product Documentation

In order to use the results and insights of your Machine Learning Script automatically, we provide you with a receiving Action Flow that integrates the ML results into secondary actions. This template shows you the steps of a ML script snippet that pushes data to a receiving Action Flow and how the Action Flow can deal with this information. If you want to know more on how to build useful Machine Learning Scripts have a look here.

Follow the step-by-step guide below to implement this solution for your Celonis Action Flows snippet.

The image below shows two working Action Flows which:

Copy the following script snippet into a new or existing ML App.

import json
import re
import sys
import requests



## you can choose any data to send to webhook, as long as it is in json format, for example
json_transfer = {'email_to': email_to,
                'info1': value_ABC,
                'info2': value_BCD
                }
data = json.dumps(json_transfer)


webhook_url = '< paste your webhook url here >'

byte_length = str(sys.getsizeof(data))
headers = {'Content-Type': "application/json", 'Content-Length': data}
response = requests.post(webhook_url, data=data_df, headers=headers)

The ML script will run your pre-defined Python logic that you either trigger manually in the ML Workbench or by a different Action Flow (see here).

Response Script Snippet

1. From your Celonis Platform team, click Data - Machine Learning.

2. Open an existing App or create a new one (click New App).

3. Import the script provided above

4. Make sure the script is uploaded successfully and displayed in the section on the left

Below you will find the step-by-step guide for configuring each module of the above Action Flow.

1. Receive ML Script Response

In this module we define a Webhook where we receive data from the ML Script.

Configuration:

Action Flows Module: Webhooks

Action: Custom webhook

Copy the address to clipboard and proceed in the next step where you add the URL to the ML Script

2. Re-determine Data Structure of the Webhook

1. Click on the blue button called 'Re- determine data structure'

2. As soon as you see it is trying to redetermine, send the triggering phrase in the Slack channel and run the triggering Flow. When the HTTP module shows the 200 code as a response it can take a few seconds

Note

Please run the script manually, the response will connect to the webhook and re-determine the data structure.

3. The webhook determined the data structure when showing you the green success statement. Click 'Ok' to save the structure and be able to continue.

3. Send Response in Slack

In this module we'll send the received data to Slack to controle if everything worked out.

Configuration:

Action Flows Module: Slack

Action: Create a Message

Enter a channel ID or name: Select from the list

Channel type: Private channel

Private channel: select the channel where you want to get the results of the ML script

Text:

title: {{fields[].title}}
value: {{fields[].value}}

You could replace the Slack module at the end with a messaging module of your choice e.g. Email or Microsoft Teams.