# Software Engineering Practices for Machine Learning

arXiv: https://arxiv.org/abs/1906.10366 · HTML: https://mlsanity.com/paper/1906.10366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach](https://mlsanity.com/paper/2604.09988/) (2026)

## Abstract (arXiv)

In the last couple of years we have witnessed an enormous increase of machine learning (ML) applications. More and more program functions are no longer written in code, but learnt from a huge amount of data samples using an ML algorithm. However, what is often overlooked is the complexity of managing the resulting ML models as well as bringing these into a real production system. In software engineering, we have spent decades on developing tools and methodologies to create, manage and assemble complex software modules. We present an overview of current techniques to manage complex software, and how this applies to ML models.