The data science community has shifted its focus from just building machine learning models to effectively deploying them into production to derive business value through data workflows. One significant challenge in this process is model drift, where a model's performance deteriorates over time due to changes in data distribution, such as those caused by unexpected events like COVID-19. Detecting and addressing drift is difficult without ground truth labels, but solutions include periodically updating models with new data, weighing recent data more heavily, and employing A/B testing to select the best-performing algorithms. Companies like Datagran are developing end-to-end workflows to automate these processes, making it easier for data professionals to manage model drift by triggering re-training when specific conditions are met. Despite the availability of many strategies to reduce drift, the lack of integrated tools and comprehensive workflows remains a significant hurdle, underscoring the need for seamless end-to-end solutions in data science.