This survey paper extracts practical considerations from recent case studies of a variety of ML applications and is organized into sections that correspond to stages of a typical machine learning workflow: from data management and model learning to verification and deployment.
In recent years, machine learning has received increased interest both as an academic research field and as a solution for real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. Our survey shows that practitioners face challenges at each stage of the deployment. The goal of this paper is to layout a research agenda to explore approaches addressing these challenges.https://arxiv.org/abs/2011.09926