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UID:pretalx-devconf-us-2025-ZXUGWY@pretalx.devconf.info
DTSTART;TZID=EST:20250920T123000
DTEND;TZID=EST:20250920T130500
DESCRIPTION:Have you ever had to deal with training machine learning models
  where the data is very large? If the data does not fit in main memory\, t
 hen how can you use GPUs if their memories are even smaller? Many of these
  cases require strategies for handling data sets. In this presentation\, w
 e will introduce DASF\, a framework that brings together lazy data loading
  techniques using Dask\, acceleration techniques using RAPIDS AI\, and oth
 er techniques that facilitate the use of large data in ML pipelines locall
 y or in HPC environments. We will also present a show case carried out wit
 h a company in the oil and gas sector.
DTSTAMP:20260315T085008Z
LOCATION:Ladd Room (Capacity 170)
SUMMARY:Training AI Models on Massive Datasets - Julio Faracco
URL:https://pretalx.devconf.info/devconf-us-2025/talk/ZXUGWY/
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