Speed up tasks such as data preprocessing at scale, training, and inference while also gaining performance.

This session explores how two AI Tools can be used as drop-in replacements for stock pandas and scikit-learn* libraries to significantly speed up key tasks in machine learning model development and deployment on CPUs instead of GPUs.

• Modin* enables data scientists to scale to distributed DataFrame processing without having to change API code.
• Intel® Extension of Scikit-learn* seamlessly accelerates scikit-learn applications for Intel CPUs and GPUs across single- and multi-node configurations.

This session covers an overview of these tools including:

• An overview of the tools—Modin and Intel Extension for Scikit-learn—and what you can do with them
• How to use Modin as a drop-in replacement for stock pandas
• How to use the scikit-learn extension as a drop-in replacement for stock scikit-learn libraries
• A live demo showcasing the performance improvements.

Skill level: Novice

Get the following tools as stand-alone versions from GitHub* or as part of AI Tools.

• Modin
• Intel Extension for Scikit-learn

• Get Started with Modin
• Performance of Modin versus pandas
• Performance of the SVC Algorithm with Intel Extension for Scikit-learn

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