Publications

A Software Engineering Perspective on Building Production-Ready Machine Learning Systems

Authors: Petra Heck, G Schouten, Luís Cruz

Published in: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry (pp. 23-54).

Abstract: This chapter discusses how to build production-ready machine learning systems. There are several challenges involved in accomplishing this, each with its specific solutions regarding practices and tool support. The chapter presents those solutions and introduces MLOps (machine learning operations, also called machine learning engineering) as an overarching and integrated approach in which data engineers, data scientists, software engineers, and operations engineers integrate their activities to implement validated machine learning applications managed from initial idea to daily operation in a production environment. This approach combines agile software engineering processes with the machine learning-specific workflow. Following the principles of MLOps is paramount in building high-quality production-ready machine learning systems. The current state of MLOps is discussed in terms of best practices and tool support. The chapter ends by describing future developments that are bound to improve and extend the tool support for implementing an MLOps approach.

 @incollection{heck2021software, author = {P. Heck and G Schouten and Luis Cruz}, booktitle = {Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry}, chapter = {2}, date-added = {2021-09-28 17:22:10 +0100}, date-modified = {2021-09-28 17:26:35 +0100}, editor = {Valentina Chkoniya}, pages = {23--54}, publisher = {IGI Global}, title = {A Software Engineering Perspective on Building Production-Ready Machine Learning Systems}, year = {2021}} 

Read me: DOI: 10.4018/978-1-7998-6985-6.ch002.