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.
@article{arxiv.1906.10366,
title = {Software Engineering Practices for Machine Learning},
author = {Peter Kriens and Tim Verbelen},
journal= {arXiv preprint arXiv:1906.10366},
year = {2022}
}