You are working as a data scientist for a global footwear retailer, responsible for predicting when an item will be out of stock based on historical inventory data. The demand for footwear is highly dynamic as it is influenced by numerous external factors such as fashion trends, seasonality, and marketing campaigns. Your goal is to develop and deploy models trained on all available data while specifically tracking performance on different subsets of data to ensure reliability before pushing to production. What is the most streamlined and reliable way to perform this validation?