A software engineering perspective on building production-ready machine learning systems

Petra Heck*, Gerard Schouten, Luís Cruz

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationHandbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
PublisherIGI Global
Number of pages32
ISBN (Electronic)9781799869863
ISBN (Print)9781799869856
Publication statusPublished - 2021


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