Operationalizing Machine Learning Using Requirements-Grounded MLOps
Paper in proceeding, 2024

[Context & Motivation] Machine learning (ML) use has increased significantly, [Question/Problem] however, organizations still struggle with operationalizing ML. [Principle results] In this paper, we explore the intersection between machine learning operations (MLOps) and Requirements engineering (RE) by investigating the current problems and best practices associated with developing an MLOps process. The goal is to create an artifact that would guide MLOps implementation from an RE perspective, aiming for a more systematic approach to managing ML models in production by identifying and documenting the goals and objectives. The study adopted a Design Science Research methodology, examining the difficulties currently faced in creating an MLOps process, identified potential solutions to these difficulties, and assessed the effectiveness of one particular solution, an artifact containing guiding Requirements Questions sorted by ML stages and practitioner roles. [Contribution] By establishing a more thorough understanding of how the two domains interact and by offering practical guidance for implementing MLOps processes from an RE perspective, this study advances both the MLOps and RE fields.

Requirements Engineering

RE

MLOps

ML

Machine Learning

Design Science

Author

Milos Bastajic

Student at Chalmers

Jonatan Boman Karinen

Student at Chalmers

Jennifer Horkoff

Software Engineering 1

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 14588 LNCS 231-248
9783031573262 (ISBN)

30th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2024
Winterthur, Switzerland,

Subject Categories

Other Mechanical Engineering

Software Engineering

Information Science

Computer Science

DOI

10.1007/978-3-031-57327-9_15

More information

Latest update

4/26/2024