AI Analytics PART 3: Walk Through the Steps to Optimize End-to-End Machine Learning Workflows
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.
This webinar 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.
- 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.
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