Exasol provides functionality to do data science and machine learning using UDF Scripts, with support for common data science and machine learning libraries in Python, R, and Java included by default. If your favorite library is not among the default libraries, you can extend any of our script language container flavors with additional libraries. For more information, see Adding New Packages to Existing Script Languages. If there is enough interest in certain libraries, we may integrate them by default in a future release.
Tutorials and examples of how you can implement your data science and machine learning workflows are available as Jupyter Notebooks in this GitHub repository. We typically orchestrate the workflows using Exasol interfaces, such as pyexasol, r-exasol or JDBC, so that we can combine data science and machine learning within the Exasol database with external machine learning tools.