DESCRIPTION

In Part 3 of this 3-part series, you get the opportunity to watch and experience hands-on exercises using all the goodies—tools, features, and capabilities—found in the AI Kit.
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Part 3 of this 3-part series shifts to “hands-on”, with presenters demonstrating the steps needed to execute key machine learning end-to-end workflows using the Intel® AI Analytics Toolkit.

AGENDA / DISCUSSION TOPICS

Highlighting optimizations in key workflow components running on Intel® architecture, including:
Intel’s integration of the OmniSciDB engine for Modin, a library that helps speed Pandas workflows by changing a single line of code.
XGBoost – An optimized, distributed, gradient-boosting library that implements ML algorithms under the Gradient Boosting framework.
Intel’s optimized implementation of Scikit-Learn – A library of simple, efficient tools for predictive data analysis through the daal4py library.
Showing the AI Kit’s ease of use and comprehensive nature as an enterprise analytics solution.
Demonstrating how to quickly test performance with a pre-built and externally available Jupyter notebook.

PARTICIPANTS

Meghana Rao, oneAPI & AI Evangelist, Intel Corporation
Anant Sinha, Software Applications Engineer, Intel Corporation
Rachel Oberman, AI Technical Consulting Engineer, Intel Corporation

Webinar/Workshop organized by: Intel

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