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UID:pretalx-good-2026-DDCDPV@pretalx.com
DTSTART;TZID=MST:20260311T164000
DTEND;TZID=MST:20260311T165000
DESCRIPTION:Large language models are now a common tool for writing code an
 d exploring ideas\, but using them on HPC systems can still be a challenge
 . To make this simpler\, I built an interactive Open OnDemand application 
 that lets users access a local LLM directly inside Jupyter Notebook using 
 the Jupyter AI extension. Behind the scenes\, user requests are routed thr
 ough a load balancer to a pool of vLLM inference servers running on Intel 
 GPUs\, which serve open-source models up to 70B parameters.\n\nIn this tal
 k\, I’ll walk through how the system works\, why we built it\, and how i
 t gives researchers easy\, reliable access to LLM assistance without relyi
 ng on external cloud services.
DTSTAMP:20260619T081944Z
LOCATION:Main Hall
SUMMARY:Accessible LLM Inference via Open OnDemand - Keegan [Texas A&M Univ
 ersity]
URL:https://pretalx.com/good-2026/talk/DDCDPV/
END:VEVENT
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UID:pretalx-good-2026-XNUVDC@pretalx.com
DTSTART;TZID=MST:20260311T165000
DTEND;TZID=MST:20260311T170000
DESCRIPTION:Machine learning now plays a very important role in a wide rang
 e of scientific fields. However many researchers in these scientific field
 s don't have machine learning expertise and may find it difficult to train
  a machine learning model themselves. \nAuto machine learning bridges this
  gap by making it very simple to train a state of the art model. Auto mach
 ine learning frameworks like AutoGluon make it such that all you need is a
  dataset and a few lines of code.  \nThe interactive application that I ha
 ve developed for Open OnDemand closes the gap further by not requiring the
  user to write any code at all. Through our application\, users can simply
  choose a dataset and their configuration\, and train the model.
DTSTAMP:20260619T081944Z
LOCATION:Main Hall
SUMMARY:Auto Machine Learning on Open OnDemand - Keegan [Texas A&M Universi
 ty]
URL:https://pretalx.com/good-2026/talk/XNUVDC/
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