As a data scientist, you may occasionally train a machine learning model to be part of a production system. Once you have completed the offline validation of the model, the next challenge often lies in effectively deploying and managing the new model in the production environment. Machine learning model deployment, also known as model rollout, refers to the process of integrating a trained ML model into an existing production environment to make predictions with new data. It is a part of a broad
