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 often involve a one-time training phase, CT emphasizes the need for models to be regularly updated with new data to maintain their accuracy and relevance. This process helps in addressing issues such as data drift and performance degradation, ensuring that models remain effective in dynamic environments. By automating the training pipeline, organizations can enhance their decision-making capabilities and respond swiftly to changing conditions in their data landscape.