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 18582560 titled 'HANDLING SYSTEM-CHARACTERISTICS DRIFT IN MACHINE LEARNING APPLICATIONS

The abstract of this patent application discusses techniques for managing input and output errors 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 outputs based on the system's functions. An error model is trained using this test data to identify errors associated with the model's inputs and outputs between different versions of the system. The error model then adjusts the model's outputs or inputs based on the identified errors.

The patent application focuses on managing input and output errors of a machine learning model in a database system.
Test data is generated from different versions of the database system to train an error model.
The error model is used to adjust the outputs or inputs of the machine learning model based on identified errors.
The innovation aims to improve the accuracy and reliability of the machine learning model in the database system.
By addressing input and output errors, the system can provide more precise results and enhance overall performance.

Potential Applications: - This technology can be applied in various industries utilizing machine learning models in database systems. - It can be used in predictive analytics, data processing, and decision-making applications. - The innovation can benefit sectors such as healthcare, finance, marketing, and more by improving the accuracy of machine learning models.

Problems Solved: - Addresses input and output errors in machine learning models within a database system. - Enhances the reliability and accuracy of the models by adjusting outputs and inputs based on error identification.

Benefits: - Improved accuracy and reliability of machine learning models. - Enhanced performance and precision in generating outputs. - Increased efficiency in data processing and decision-making.

Commercial Applications: Title: Enhanced Error Management for Machine Learning Models in Database Systems This technology can be commercially used in industries such as healthcare for predictive analytics, finance for risk assessment, marketing for targeted advertising, and more. The innovation can lead to more accurate and reliable machine learning models, improving overall performance and decision-making processes.

Questions about Enhanced Error Management for Machine Learning Models in Database Systems: 1. How does this technology impact the accuracy of machine learning models in database systems? 2. What are the potential applications of this innovation in different industries?

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.