What is the process of updating a deployed model by retraining it on new data called?

The process of updating a deployed model by retraining it on new data is referred to as model retraining. This involves taking the existing model and improving its performance or adapting it to changes in data distribution by using new data that reflects more recent trends or patterns. Model retraining helps ensure that the AI system remains accurate and relevant over time, especially in dynamic environments where input data can shift due to various factors, such as seasonal changes, evolving user behaviors, or ongoing developments in the subject matter relevant to the model.

Model tuning pertains to adjusting hyperparameters or configuration settings to optimize the model's performance on a specific dataset, but it does not involve updating the model with new data. Model validation involves assessing the model's performance using a separate dataset to ensure its accuracy and reliability, which occurs before deployment or during the training process rather than after deployment. Multiclass classification refers to a type of problem in machine learning where the model predicts instances belonging to one of several classes, but it does not describe the process of updating or retraining models.