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Data preprocessing is a fundamental part of any machine learning applicationand frequently the most time-consuming aspect when developing a machinelearning solution. Preprocessing for deep learning is characterized bypipelines that lazily load data and perform data transformation, augmentation,batching and logging. Many of these functions are common across applicationsbut require different arrangements for training, testing or inference. Here weintroduce a novel software framework named nuts-flow/ml that encapsulatescommon preprocessing operations as components, which can be flexibly arrangedto rapidly construct efficient preprocessing pipelines for deep learning.

nuts-flow/ml: data pre-processing for deep learning