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DTSTART:20221030T030000
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SUMMARY:Shrinking gigabyte sized scikit-learn models for deployment - Pave
 l Zwerschke\, Yasin Tatar
DTSTART;TZID=Europe/Berlin:20230419T105000
DTEND;TZID=Europe/Berlin:20230419T112000
DTSTAMP:20261009T011505Z
UID:pretalx-pyconde-pydata-berlin-2023-7EUPC3@pretalx.com
DESCRIPTION:We present an open source library to shrink pickled scikit-lea
 rn and lightgbm models. We will provide insights of how pickling ML models
  work and how to improve the disk representation. With this approach\, we 
 can reduce the deployment size of machine learning applications up to 6x.
LOCATION:B09
URL:https://pretalx.com/pyconde-pydata-berlin-2023/talk/7EUPC3/
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