MLOps - Feature Engineering for Tabular Data With GPU Acceleration

Explore advanced feature engineering techniques for tabular data using GPU acceleration through practical notebook demonstrations with RAPIDS cuDF and cuML. Learn to implement target encoding and count encoding methods while training models with XGBoost and Support Vector Machines. Compare CPU versus GPU performance across different feature engineering workflows to understand the computational advantages of GPU-accelerated data processing. Master the integration of RAPIDS libraries into your machine learning pipeline for enhanced performance on tabular datasets. Gain hands-on experience with real-world examples that showcase the speed improvements possible when leveraging GPU computing for data preprocessing and model training tasks.