A Coding Guide to Build a Scalable End-to-End Machine Learning Data Pipeline Using Daft for High-Performance Structured and Image Data Processing

This guide demonstrates building scalable machine learning pipelines using Daft, a Python-native data engine. It covers loading MNIST datasets, applying transformations via UDFs, feature engineering, aggregations, and joins with lazy execution. The tutorial shows how to efficiently combine structured data processing, numerical computations, and image handling in a single end-to-end analytical pipeline for high-performance data workflows.