The article provides a comprehensive guide to building an end-to-end analytics and machine learning pipeline using Vaex, focusing on scalability for millions of rows. It covers data loading, preprocessing, feature engineering, model training, evaluation, and deployment. Key steps include handling categorical variables, standardizing numeric features, creating derived features like decile rankings, and exporting reproducible artifacts. The guide emphasizes Vaex's capabilities in efficient memory management and out-of-core execution for large datasets while supporting advanced analytics tasks and sklearn integration.
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