Architectural Design Decisions for Machine Learning Deployment

Abstract

Deploying machine learning models to production is challenging, partially due to the misalignment between software engineering and machine learning disciplines but also due to potential practitioner knowledge gaps. To reduce this gap and guide decision-making, we conducted a qualitative investigation into the technical challenges faced by practitioners based on studying the grey literature and applying the Straussian Grounded Theory research method. We modelled current practices in machine learning, resulting in a UML-based architectural design decision model based on current practitioner understanding of the domain and a subset of the decision space and identified seven architectural design decisions, various relations between them, twenty-six decision options and forty-four decision drivers in thirty-five sources. Our results intend to help bridge the gap between science and practice, increase understanding of how practitioners approach deployment of their solutions, and support practitioners in their decision-making.