After the model training and evaluation processes are complete, the best-performing models are deployed to production. Model deployment is a way to integrate a model into an existing production environment to make practical business decisions based on data.
Model deployment is one of the last stages in the model lifecycle and one of the most difficult processes of gaining value from machine learning. It requires coordination between Data Scientists, IT teams, software developers, and business professionals to ensure the model works reliably in the organization’s production environment.
The ModelOps UI provides you an effective and seamless method to deploy your models into production that helps you start using them to make practical decisions.