Challenges in Deploying Machine Learning: a Survey of Case Studies
This paper identifies common deployment challenges for machine learning practitioners, aiming to lay out a research agenda for addressing these issues.
This survey paper identifies challenges in deploying machine learning models by reviewing published case studies across various industries and applications. It maps these challenges to each stage of the machine learning deployment workflow, indicating that practitioners encounter issues throughout the entire process.
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.