HANDLING SYSTEM-CHARACTERISTICS DRIFT IN MACHINE LEARNING APPLICATIONS

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HANDLING SYSTEM-CHARACTERISTICS DRIFT IN MACHINE LEARNING APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240232722 titled 'HANDLING SYSTEM-CHARACTERISTICS DRIFT IN MACHINE LEARNING APPLICATIONS

The abstract discusses techniques for managing input and output error of a machine learning model in a database system. Test data is generated from successive versions of the database system, which includes a machine learning model to generate an output corresponding to a function of the system. The test data is used to train an error model to determine errors associated with the output or input to the machine learning model between the different versions of the system. The error model adjusts the output or input of the machine learning model based on the errors identified.

The innovation involves using test data from different versions of a database system to train an error model for managing input and output errors of a machine learning model.
The error model adjusts the output of the machine learning model when errors are associated with the output and adjusts the input when errors are associated with the input.
This approach helps improve the accuracy and reliability of the machine learning model by addressing errors in the input and output data.
By continuously updating the error model based on test data from successive versions of the database system, the machine learning model can adapt to changes and improve performance over time.
The system provides a systematic way to handle errors in machine learning models within a database environment, enhancing the overall efficiency and effectiveness of the system.

Potential Applications: - This technology can be applied in various industries where machine learning models are used in database systems, such as healthcare, finance, and e-commerce. - It can help improve the accuracy of predictive analytics, recommendation systems, and other machine learning applications that rely on data from database systems.

Problems Solved: - Addresses the challenge of managing input and output errors in machine learning models within a database system. - Provides a systematic approach to identify and correct errors in the input and output data of the machine learning model.

Benefits: - Improves the accuracy and reliability of machine learning models by addressing errors in input and output data. - Enhances the performance of machine learning applications by continuously updating error models based on test data from successive versions of the database system.

Commercial Applications: - Title: "Enhancing Machine Learning Model Performance in Database Systems" - This technology can be commercialized as a software solution for companies using machine learning models in database systems. - It can be marketed to industries such as healthcare, finance, and e-commerce to improve the efficiency and accuracy of their machine learning applications.

Questions about the Technology: 1. How does the error model adjust the output of the machine learning model based on identified errors? 2. What are the potential long-term benefits of using this technology in database systems?

Original Abstract Submitted

techniques for managing input and output error of a machine learning (ml) model in a database system are presented herein. test data is generated from successive versions of a database system, the database system comprising a machine learning (ml) model to generate an output corresponding to a function of the database system the test data is used to train an error model to determine an error associated with the output of or an input to the ml model between the successive versions of the database system. in response to the ml model generating a first output based on a first input: the error model adjusts the first output when the error is associated with the output to the ml model and adjusts the first input when the error is associated with the input to the ml model.