What does model retraining in the context of PSE Cortex involve?

Model retraining in the context of PSE Cortex involves the process of updating machine learning models with new data. This is an essential practice in machine learning, as it allows models to adapt to new information, patterns, and trends that emerge after the initial training phase. As data continuously evolves, retraining ensures that the model maintains its accuracy and relevance, enhancing its predictive performance based on the most current data available.

By incorporating new data into the model's training set, practitioners can improve the model's understanding of data distributions and relationships that may not have been present when it was first trained. This ongoing cycle of retraining is crucial for applications where the input data can change over time, thereby reinforcing the model's utility and effectiveness.

Retraining helps in countering issues related to model drift, where the original model's assumptions may no longer apply due to shifts in data characteristics. This continuous improvement process is vital for maintaining robust machine learning applications in a dynamic environment.