Speed up tasks such as data preprocessing at scale, training, and inference while also gaining performance. This session explores how two Intel® 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. - Intel® Distribution of 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—Intel Distribution of Modin and Intel Extension for Scikit-learn—and what you can do with them - How to use Intel Distribution of 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