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Celonis Product Documentation

Machine Learning

The Celonis Platform offers integrated and embedded machine learning capabilities, giving you access to a fully hosted and managed machine learning workbench (MLWB). This workbench is an integrated Python development environment based on Jupyter Notebook. These integrated tools require no installation, no maintenance, and no server requests.

You can access the machine learning features by clicking Data - Machine Learning:

Data_-_Machine_learning.png

Machine learning workbench activity types

When using the machine learning workbench, you can choose from:

  • Notebooks and console

    • Python3 (lpykernel)

    • Julia 1.5.3

    • Python 3.8 (XPython raw)

    • Python 3.8 (XPython)

    • R

  • Other

    • Terminal

    • Text file

    • Markdown file

    • Julia file

    • Python file

    • R file

Celonis Python packages

Celonis also offers a pre-installed Python package called PyCelonis, enabling you to use your Celonis Platform data when creating Machine Learning notebooks.

The PyCelonis package

PyCelonis is a Python-based API wrapper for the Celonis Platform. With this package you can interact with Celonis objects as native objects; for example, copy an analysis, pull and push data, or reload a data model.

For more information about PyCelonis, see: PyCelonis documentation .

And for a set of tutorials on how to use PyCelonis, see: PyCelonis tutorials .

The PyCelonis example repository

The PyCelonis example repository contains demo notebooks covering popular PyCelonis examples and use cases. The repository contains notebooks for both PyCelonis 1.X and 2.X. They show what you can achieve using PyCelonis. The examples are grouped by their PyCelonis version and by specific use cases.