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

arXiv: https://arxiv.org/abs/1708.06046 · HTML: https://mlsanity.com/paper/1708.06046/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing](https://mlsanity.com/paper/2509.10712/) (2025)
- [MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds](https://mlsanity.com/paper/2508.03676/) (2025)

## Abstract (arXiv)

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.