Continuous Training (CT) refers to the ongoing process of updating machine learning models with new data to enhance their performance and adaptability. Unlike traditional training methods, which involve a fixed dataset and a one-time training phase, CT allows models to learn incrementally as new information becomes available. This approach is particularly beneficial in dynamic environments where data patterns may change over time. By implementing CT, organizations can ensure that their models remain relevant and effective, ultimately leading to improved decision-making and operational efficiency. Continuous Training is a key component of Continuous Machine Learning, facilitating seamless integration of new insights into existing models.