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Very Large Datasets with the GPU Data Frame
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QCon London International Software Development Conference returns on April 8-10, 2024. Level-up on 15 major software and leadership topics including; The Tech of FinTech, What's Next in GenAI and Large Language Models (LLMs), Performance Engineering, Architecture for the Age of AI, Innovations in Data Engineering and more.
Learn the emerging trends. Explore the use cases. Implement the best practices.
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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.
Learn the emerging trends. Explore the use cases. Implement the best practices.
-------------------------------------------------------------------------------------------------------------------------------------------------------
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.
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