You are working as a machine learning engineer for a car dealership company. Your task is to develop an ML model to predict the cost of used automobiles. The prediction is based on various features such as location, condition, model type, color, and engine/battery efficiency. The data is updated every night, and car dealerships will rely on your model to determine appropriate car prices. You have created a Vertex AI pipeline that reads the data, splits it into training, evaluation, and test sets, performs feature engineering, trains the model using the training dataset, and validates the model using the evaluation dataset. Now, you need to design a retraining workflow that minimizes costs while ensuring model performance remains high. What strategy should you adopt?