The Three Hidden Issues Preventing Your Machine Learning Deployments

Getting machine learning models into production continues to plague organizations. To help combat this issue, business leaders, data scientists, and IT teams need to work together to identify a solution to the three hidden issues preventing deployment: production monitoring, model updates, and modern governance. The deployment of machine learning models in production is notoriously difficult. While deployment is still an issue, I would argue that it is one that has mostly been solved by the use of containers and container management systems like Docker and Kubernetes. With a critical technical challenge removed, you would expect more models to start to make it into production.