You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible. What should you do?

TabNet uses sequential attention that promotes model interpretability and Tabular Workflows is a set of integrated, fully managed, and scalable pipelines for end-to-end ML with tabular data for regression and classification. Tabular Workflows for Wide & Deep is optimized for memorization and generalization, not interpretability. Building pipelines on Cloud Composer or GKE takes much longer, violating the goal to productionize quickly.