Requirements Grounded MLOps - A Design Science Study
Examensarbete för masterexamen
Software engineering and technology (MPSOF), MSc
Boman Karinen, Jonatan
The use of Machine learning (ML) has increased significantly in recent years, however, organizations still struggle with operationalizing ML. In this thesis, we explore the intersection between machine learning operations (MLOps) and Requirements engineering (RE) by investigating the current best practices, challenges, and potential solutions associated with developing an MLOps process. The goal of this thesis was to create an artifact that would guide MLOps implementation from an RE perspective, resulting in 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, which comprised investigating three research questions while the design artifact was being created in parallel. The research questions examined the difficulties currently faced in creating an MLOps process, identified potential solutions to these difficulties, and assessed the effectiveness of these solutions. The study was conducted in three cycles, with each cycle answering all research questions but focusing mainly on one specific question, allowing for the initial creation and subsequent refinement of the artifact based on data collected during each cycle. By establishing a more thorough understanding of how the two domains interact and by offering practical guidance for implementing MLOps processes from a RE perspective, this study advances both the MLOps and RE fields. Quality feedback was collected on the artifact in the form of theoretical evaluations. However, the main shortcoming of the study is the lack of evaluation of the artifact’s effectiveness under real-world conditions. Therefore, a recommendation for further research is to conduct case studies testing the artifact in real-world settings to evaluate its effectiveness and improve upon its limitations.
Machine learning operations , Machine learning , Requirements engineering , ML , RE , MLOps , Design science research