Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.

Abstract: Dynamic management of multiple persistent data stores is disclosed. One or more data objects are retrieved from two or more of a plurality of persistent data stores and provided to the client context in a manner such that the one or more data objects appear to the client context to come from a single source, even if in fact the objects have been retrieved from two or more different persistent stores.

Abstract: Dynamic management of multiple persistent data stores is disclosed. One or more data objects are retrieved from two or more of a plurality of persistent data stores and provided to the client context in a manner such that the one or more data objects appear to the client context to come from a single source, even if in fact the objects have been retrieved from two or more different persistent stores.

Abstract: Dynamic management of multiple persistent data stores is disclosed. One or more data objects are associated with a client context, e.g., an instance of a client application program. The one or more data objects are retrieved from one or more of a plurality of persistent data stores and provided to the client context in a manner such that the one or more data objects appear to the client context to come from a single source, even if in fact the objects have been retrieved from two or more different persistent stores.

Abstract: Dynamic management of multiple persistent data stores is disclosed. One or more data objects are associated with a client context, e.g., an instance of a client application program. The one or more data objects are retrieved from one or more of a plurality of persistent data stores and provided to the client context in a manner such that the one or more data objects appear to the client context to come from a single source, even if in fact the objects have been retrieved from two or more different persistent stores.

Abstract: Dynamic management of multiple persistent data stores is disclosed. One or more data objects are associated with a client context, e.g., an instance of a client application program. The one or more data objects are retrieved from one or more of a plurality of persistent data stores and provided to the client context in a manner such that the one or more data objects appear to the client context to come from a single source, even if in fact the objects have been retrieved from two or more different persistent stores.

Abstract: Managing cross-store relationships to data objects is disclosed. Specifically, cross-store relationships to data objects stored in a potentially dynamically changing set of persistent data stores is provided through a relationship definition that identifies a type of object to which the relationship pertains and specifies a property to be used to determine, which, if any, objects of the identified type are to be included in the relationship; by determining dynamically which, if any, currently available objects of the identified type have the specified property; and making available from among the objects of the identified type available at that time, if any, those objects, if any, that have the specified property.