An interoperable platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have few well known plug types. The building blocks can be composed to build complex compositions. Interoperability between data formats, metadata schema and interfaces to machine learning (ML) functions and trained machine learning models can be provided with no loss of information. A cloud runtime environment can be provided in which the composed workflows can be hosted as REST API to run in production.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: at least one processor: a memory connected to the at least one processor; and at least one program module providing interoperability between a first machine learning execution environment executing in a first programming language and a second execution environment executing in a second programming language wherein the interoperability enables existing tools written in the second programming language to be used in the first machine learning execution environment without reprogramming. 2. The system of claim 1, further comprising: at least one program module that translates a machine learning-specific schema associated with the first machine learning execution environment to a schema associated with the second execution environment without loss of information. 3. The system of claim 1, further comprising: at least one program module that translates a machine learning-specific schema associated with the second execution environment to a schema associated with the first machine learning execution environment without loss of information. 4. The system of claim 1, wherein the second programming language is one of R, JAVA or Python. 5. The system of claim 4, wherein an R factor data type associated with the second programming language is converted to a categorical data type associated with the first programming language. 6. The system of claim 4, wherein an R missing value type associated with the second programming language is converted to a missing value associated with the first programming language. 7. The system of claim 3, wherein machine-learning specific metadata is not lost when execution passes from one execution environment to a second execution environment. 8. The system of claim 3, wherein machine-learning schema comprises metadata about feature columns, labels, scores and weights. 9. A method comprising: providing interoperability between a first machine learning execution environment executing in a first programming language and a second execution environment executing in a second programming language wherein the interoperability enables existing tools written in the second programming language to be used in the first machine learning execution environment without reprogramming. 10. The method of claim 8, wherein the programming language of the second execution environment is R. 11. The method of claim 8, wherein the programming language of the second execution environment is Python. 12. The method of claim 8, wherein the programming language of the second execution environment is JAVA. 13. The method of claim 8, wherein the data types of the programming language of the second execution environment are converted into .NET data types in accordance with an extensible data table. 14. A computer-readable storage medium comprising computer-readable instructions which when executed cause at least one processor of a computing device to: enable existing tools in a plurality of programming languages to be used automatically without conversion coding in a machine learning execution environment. 15. The computer-readable storage medium of claim 14, comprising further computer-readable instructions which when executed cause the at least one processor to: map a scripting language schema of a first programming language to a schema in a data table in a second programming language. 16. The computer-readable storage medium of claim 14, comprising further computer-readable instructions which when executed cause the at least one processor to: map a scripting language schema to a schema in a data table in a machine learning execution environment, wherein the scripting language is R. 17. The computer-readable storage medium of claim 14, comprising further computer-readable instructions which when executed cause the at least one processor to: map a scripting language schema to a schema in a data table in a machine learning execution environment, wherein the scripting language is Python. 18. The computer-readable storage medium of claim 14, comprising further computer-readable instructions which when executed cause the at least one processor to: map a scripting language schema to a schema in a data table in a machine learning execution environment, wherein the data table is extensible. 19. The computer-readable storage medium of claim 14, comprising further computer-readable instructions which when executed cause the at least one processor to: map 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. 20. The computer-readable storage medium of claim 14, comprising further computer-readable instructions which when executed cause the at least one processor to: wrap the scripting language code in a .NET wrapper.