Speaker "Veda Shankar " Details Back



End-to-End Open Source Data Science Workflow Using OmniSci and Nvidia RAPIDS


Learn how the OmniSci's open source GPU-Accelerated SQL engine fits into the overall Nvidia RAPIDS data science framework. In this session I will show you how to create this open source GPU analytics stack which includes deploying OmniSci analytics platform and RAPIDS libraries. We will learn to contruct an end-to-end data science pipeline completely in the GPU from ingesting data to running SQL queries, and passing the results of the query as a GPU dataframe (CuDF) for data manipulation and for feature engineering using machine learning libraries.


Veda Shankar is a Senior Developer Advocate at MapD working actively to assist the user community to take advantage of MapD’s open source analytics platform. He is a customer oriented IT specialist with a unique combination of experience in product development, marketing and sales engineering. Prior to MapD, Veda worked on various open source software defined data center products at Red Hat.