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UID:pretalx-scipy2025-ZAKQHP@cfp.scipy.org
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DESCRIPTION:Pandas and scikit-learn have become staples in the machine lear
 ning 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+ execu
 tion backends\, including dataframe libraries\, databases\, and analytics 
 engines. Ibis enables users to leverage these powerful tools without rewri
 ting their data engineering code (or learning SQL). IbisML extends the ben
 efits of using Ibis to the ML workflow by letting users preprocess their d
 ata at scale on any Ibis-supported backend.\n\nIn this tutorial\, you'll b
 uild an end-to-end machine learning project to predict the live win probab
 ility after each move during chess games.
DTSTAMP:20260614T133653Z
LOCATION:Room 315
SUMMARY:Building machine learning pipelines that scale: a case study using 
 Ibis and IbisML - Deepyaman Datta\, Anjali Datta
URL:https://cfp.scipy.org/scipy2025/talk/ZAKQHP/
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