US Patent Application 17738110. MACHINE LEARNING MODEL MANAGEMENT AND SOFTWARE DEVELOPMENT INTEGRATION simplified abstract
Contents
MACHINE LEARNING MODEL MANAGEMENT AND SOFTWARE DEVELOPMENT INTEGRATION
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Patrick W. J. Evans of Fall City WA (US)
Debadeepta Dey of Kenmore WA (US)
MACHINE LEARNING MODEL MANAGEMENT AND SOFTWARE DEVELOPMENT INTEGRATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17738110 titled 'MACHINE LEARNING MODEL MANAGEMENT AND SOFTWARE DEVELOPMENT INTEGRATION
Simplified Explanation
- The patent application describes a method for automatically generating a model wrapper for a machine learning (ML) model identified within the source code of a software project. - This model wrapper can be used during the compilation and execution of the software code, making it easier to integrate ML models into software projects. - A representative object is also generated to manage the ML model during the software development process, allowing for easier management and manipulation of the model attributes. - The runtime library associated with the ML model is automatically included in the software project, simplifying the integration process. - The training of the ML model can also be automatically initiated, ensuring that the model is up-to-date and accurate. - In cases where the ML model is still being trained, a placeholder or partially trained model can be used during software development, allowing for continued development without waiting for the model to be fully trained.
Original Abstract Submitted
In examples, a declaration of an ML model is identified within source code of a software project. As a result, a model wrapper may be generated for the ML model and used when compiling and/or executing the software code. Further, a representative object may be generated to enable management of the ML model during the software development process. As an example, model attributes associated with the ML model may be identified from the software code and used to manage the ML model accordingly. In examples, a runtime library associated with the ML model may be automatically included in the software project and/or training of the ML model may be automatically initiated. In some instances, a placeholder ML model or a partially trained or intermediate ML model may be used when building and executing the software project while the ML model is still being trained, thereby enabling continued software development.
