  In recent years, machine learning has transitioned from a field of academic
research interest to a field capable of solving 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. By mapping found challenges to the
steps of the machine learning deployment workflow we show that practitioners
face issues at each stage of the deployment process. The goal of this paper is
to lay out a research agenda to explore approaches addressing these challenges.
