Data Engineering for ML

Most ML projects rise or fall on the quality of the data pipeline, not the cleverness of the model. In Python, that pipeline usually starts life as a Pandas notebook and graduates into something that feeds PyTorch cleanly, repeatably, and at speed. This chapter builds that path: we’ll move from DataFrames to Arrow/Parquet, construct Dataset/DataLoader pairs that don’t leak or stall, stream data with torchdata, and lean on domain toolkits like torchvision, torchaudio, and torchtext. Along the way, we’ll talk about weak supervision – programmatic labels when gold labels are scarce – and show how to produce safe synthetic data with Faker or an LLM workflow.