Continuous Training (CT) is an essential practice in machine learning and data science that focuses on the ongoing improvement and adaptation of models over time. Unlike traditional training methods, which may involve a one-time training phase, CT emphasizes the need for models to be regularly updated with new data and insights. This approach ensures that models remain relevant and effective in dynamic environments, addressing issues such as data drift and changing user behavior. By implementing Continuous Training, organizations can enhance model performance, maintain accuracy, and streamline the deployment process, ultimately leading to better decision-making and outcomes.