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