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SUMMARY:Building an ML pipeline for churn prediction with skrub Data Ops -
  Riccardo Cappuzzo
DTSTART;TZID=Europe/Paris:20261126T144000
DTEND;TZID=Europe/Paris:20261126T151000
DTSTAMP:20260930T112609Z
UID:pretalx-compute-paris-2026-GB7TQV@pretalx.com
DESCRIPTION:Skrub is a package that eases preparing dataframes so they can
  be used in machine-learning tasks. In practice\, data can be spread over 
 multiple tables\, represent various types of information (numbers\, text\,
  dates\, images...)\, or be stored on external database systems rather tha
 n dataframes. Crucially\, data preparation often involves different steps 
 at training or prediction time as stateful pre-processing steps (where par
 t of the preparation is learned or adjusted on the train data e.g.\, imput
 ation).\n\nSkrub [Data Ops](https://skrub-data.org/stable/auto_tutorials/1
 110_data_ops_intro.html) help constructing complex pipelines to handle thi
 s variety of scenarios\, while at the same time avoiding data leakage and 
 allowing to tune the parameters of the full preprocessing pipeline to maxi
 mize the performance of the final machine learning model. \n\nIn this talk
 \, we give a brief introduction of the Data Ops framework before presentin
 g practical use cases\, ranging from churn prediction\, to energy usage fo
 recasting\, to image processing. Data Ops simplify greatly training models
  in advanced\, multi-table situations. \n\nBy the end of the talk\, attend
 ees will learn about the skrub Data Ops to build data preparation and feat
 ure engineering pipelines that assemble rich transformations across multip
 le tables.
LOCATION:Auditorium
URL:https://pretalx.com/compute-paris-2026/talk/GB7TQV/
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