Pandas and scikit-learn have become staples in the machine learning toolkit for processing and modeling tabular data in Python. However, when data size scales up, these tools become slow or run out of memory. Ibis provides a unified, Pythonic, dataframe-like interface to 20+ execution backends, including dataframe libraries, databases, and analytics engines. Ibis enables users to leverage these powerful tools without rewriting their data engineering code (or learning SQL). IbisML extends the benefits of using Ibis to the ML workflow by letting users preprocess their data at scale on any Ibis-supported backend.

In this tutorial, you'll build an end-to-end machine learning project to predict the live win probability after each move during chess games.