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:
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.
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, such as copying an analysis, pulling and pushing data, or reloading a data model.
For more information about PyCelonis, see PyCelonis documentation.
For tutorials on how to use PyCelonis, see PyCelonis tutorials.
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.