Continuous Training (CT) refers to the ongoing process of updating and refining machine learning models to ensure they remain effective and relevant over time. This approach is essential in dynamic environments where data patterns can change, necessitating regular adjustments to models. CT involves automating the training process, allowing data scientists to integrate new data, retrain models, and evaluate performance continuously. By implementing CT, organizations can enhance model accuracy, reduce downtime, and ensure that their machine learning solutions adapt to evolving business needs. This practice is crucial for maintaining competitive advantage in data-driven industries.