Abstract: A hybrid code construct combines imperative and declarative semantics for leveraging complementary features for a relational database exchange optimized for declarative access driven by imperative direction for imposing iterative and conditional looping behavior. Databases responsive to a declarative command structure receive declarative code generated from an imperative code sequence. The imperative code is based on a language that allows conditional iteration for repetitive commands, which invoke the declarative command syntax within a controlled loop or iteration. Declarative syntax, while enjoying optimizations for high performance database access, does not lend itself well to iterative logic common for linear regression and training of ML models. The hybrid code allows efficient ML training while the dataset defining the model remains within the database and need not incur network transport for Al based usage.