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Datapipeline issue between Celonis Studio and Machine LEarnign Workbench

  • July 6, 2026
  • 2 replies
  • 36 views
Sagar_MTB
Level 1
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Hi Guys,

I am currently facing issues with the datapipeline between celonis studio and the machine learning workbench. The ML Workbench is unable to access Celonis data due to outdated celonis_ml dependencies that rely on deprecated sklearn functions. This breaks the built-in data pipeline and prevents direct data model access. As a workaround, data had to be exported manually as CSV and processed externally. An update to the ML environment and data connectors is required to restore full functionality.

Is there a fix or a workaround for this?

2 replies

S
Celonaut
  • Shiven
  • Celonaut
  • July 6, 2026

This is a  issue that usually stems from a conflict between legacy packages (celonis_ml or older pycelonis 1.x versions) and updated Python/scikit-learn ecosystems. Because older Machine Learning Workbench (MLWB) environments cannot be upgraded in-place, the underlying dependencies eventually break.

Short-term workarounds and the recommended long-term fix:

 

If you need to patch your current notebook immediately, try the following in your MLWB terminal:

  • Fix the sklearn dependency naming block: Older installers explicitly look for the deprecated sklearn package name instead of scikit-learn. Try explicitly installing scikit-learn first, then installing your package:

    Bash

     

    pip install scikit-learn

  • Target a stable legacy version: If you are hitting InvalidVersion or signature errors, completely clean out and target a stable 1.x release of PyCelonis:

    Bash

     

    pip uninstall pycelonis -y
    pip install pycelonis==1.7.6  # or 1.7.7+ depending on your specific error

2. The Recommended Long-Term Fix (Environment Refresh)

Since Celonis does not support in-place upgrades for older MLWB underlying instances, the cleanest path forward is to migrate to a fresh environment and modern syntax:

  1. Backup your work: Commit your current notebooks/code to Git or export them as a ZIP file.

  2. Provision a new MLWB: Create a brand-new Workbench instance from the Celonis Studio overview page. Newly provisioned workbenches automatically inherit the latest Python environment and updated core dependencies.

  3. Migrate to Modern PyCelonis: Move away from the legacy celonis_ml wrapper and use the native pycelonis API to pull data models directly. For example:

     

     

    gagan1
    Member Spotlight
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    • gagan1
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    • July 6, 2026

    Hey ​@Sagar_MTB , 

    Current version is 2.x (the 1.x line is deprecated). If you hit dependency issues during the upgrade, the cleanest path is actually to provision a fresh MLWB environment from Celonis Studio — newly provisioned workbenches come with the latest pycelonis and all dependencies pre-installed and pre-configured.

    Docs for reference:
    https://celonis.github.io/pycelonis/latest/
    https://docs.celonis.com/en/getting-started-with-the-ml-workbench.html

    Gagan
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