Continuous Training (CT) is an essential practice in machine learning and artificial intelligence that focuses on the ongoing improvement of models through regular updates and retraining. This approach ensures that models remain accurate and relevant by adapting to new data and changing conditions over time. By automating the training process, organizations can efficiently manage model performance, detect data drift, and respond to performance degradation. Continuous Training is a key component of MLOps, enabling teams to maintain high-quality models and streamline their deployment in production environments, ultimately enhancing decision-making and operational efficiency.