Challenges in Deploying Machine Learning Solutions

This document is a survey report that reviews the challenges associated with deploying machine learning (ML) models in production systems. It outlines the deployment workflow stages, including data management, model learning, model verification, and model deployment, and highlights the issues practitioners face at each stage. The report discusses various use case studies across different industries, extracting practical considerations and common problems encountered during deployment. Ethical considerations, user trust, and security are also addressed as cross-cutting aspects that affect all stages of the deployment process. The survey aims to raise awareness in the academic community about the practical challenges faced by practitioners and proposes a research agenda to explore potential solutions to these deployment issues. The findings are based on a review of recent literature and case studies, emphasizing the need for further research in this evolving field.