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UID:pearc_PEARC19_sess192_pap197@linklings.com
SUMMARY:Publishing and Serving Machine Learning Models with DLHub
DESCRIPTION:Publishing and Serving Machine Learning Models with DLHub\n\nC
 hard, Ward, lI, Babuji, Woodard...\n\nIn this paper, we introduce the Data
  and Learning Hub for Science (DLHub). DLHub serves as a nexus for publish
 ing, sharing, discovering, and reusing machine learning models. It provide
 s a flexible publication platform that enables researchers to describe and
  deposit models by associating publication and model-specific metadata and
  assigning a persistent identifier for subsequent citation. DLHub also sup
 ports scalable model inference, allowing researchers to execute inference 
 tasks using a distributed execution engine, containerized models, and Kube
 rnetes. Here we describe DLHub and present four scientific use cases that 
 illustrate how DLHub can be used to reliably, efficiently, and scalably in
 tegrate ML into scientific processes\n\nPaper\nTechnical Paper, Machine Le
 arning/Artificial Intelligence, Adoption
URL:https://pearc19.conference-program.com/presentation/?id=pap197&sess=se
 ss192
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