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.
- 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.
- Read the latest Intel AI Analytics blogs on Medium.
- Develop in the Cloud—Sign up for an Intel® DevCloud account, a free development sandbox with access to the latest Intel® hardware and oneAPI software.
- Subscribe to the POD—Code Together is an interview series that explores the challenges at the forefront of cross-architecture development. Each bi-weekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Listen and subscribe today.