Machine Learning and Data Munging in H2O Driverless AI with datatable

Hello Makers!

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Join us as we discuss machine learning and data munging in H2O Driveless AI with datatable. Following is a brief agenda for the evening:

9:30 - 10:00 AM: Doors open for networking and refreshments

10:00 - 10:45 AM: Presentation by Parul Pandey

10:45 - 11:00 AM: Presentation by Jan Gorecki

11:00 - 11:30 AM: Q&A

11:30 - 12:00 PM - Networking

Description:

H2O datatable is a Python package for manipulating 2-dimensional tabular data structures, aka data frames. It is close in spirit to pandas, however, we put specific emphasis on speed and big data support. As the name suggests, the package is closely related to R's data.table and attempts to mimic its core algorithms and API. H2O datatable started in 2017 as a toolkit for performing big data operations on a single-node machine, at the maximum speed possible. Such requirements are dictated by modern machine-learning applications, such as H2O Driverless AI, which need to process large volumes of data and generate many features in order to achieve the best model accuracy. In the talk we will introduce H2O datatable, focusing on its data munging and modeling capabilities, followed by the Q/A session.

Jan in his short talk will present h2o.ai’s continuous benchmark project for data munging tools. Python’s datatable is among tools presented in benchmark reports thus audience will be able to get a better understanding about the scalability of datatable package.

Speaker Bio:

Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science, evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.