Very Large Datasets with the GPU Data Frame

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Veda Shankar explains how the GDF technology works, shows how it is enabling a diverse set of GPU workloads, and demonstrates how to use a Jupyter Notebook to take advantage of it. He demonstrates on a very large dataset how to manage a full Machine Learning Pipeline with minimal data exchange overhead between MapD’s SQL engine and H2O’s generalized linear model library (GLM).
Veda Shankar is a Developer Advocate at MapD working actively to assist the user community to take advantage of MapD’s open source analytics platform.
This video was recorded at QCon.ai 2018: bit.ly/2piRtLl
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[@scarface548]

screen was too small..couldn't see anything in dropdowns ect. Will try their demo on their website.

[@iamkrishradha]

little insecure presentation...