14. A method, including: composing the one or more modules of a machine learning workflow into at least a portion of a machine learning application; automatically interfacing a machine learning dataset, while processing the machine learning dataset with the composed machine learning application running on an interoperability platform, between a first execution environment configured to execute machine learning code in a first language for the composed machine learning application and a second execution environment configured to execute code written in a second language for the composed machine learning application; and automatically interfacing metadata, while processing the machine learning dataset with the composed machine learning application running on the interoperability platform, between the first execution environment configured to execute the machine learning code in the first language for the composed machine learning application and the second execution environment configured to execute the code written in the second language for the composed machine learning application.
19. The method of claim 14 , further comprising: mapping a scripting language schema to a schema in a data table in a machine learning execution environment, wherein the data types of the scripting language are converted into .NET data types in accordance with an extensible data table.