Common Data Object for Machine Learning Model Compliance
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Solution Overview
Problem
Conventional systems lack transparency and efficiency in managing machine-learning models, particularly in ensuring compliance with system requirements frameworks due to the 'black box' nature of these models, leading to challenges in tracking usage and verifying data inputs and outputs.
Innovation Solution
A machine-learning management system that utilizes a common data object to represent implementation details of machine-learning models, performing data configuration validation against system requirements frameworks, and providing graphical user interfaces for managing and modifying models to ensure compliance and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-learning models are used to automate computing processes, then productivity is improved, but transparency and control of model usage deteriorate
Solution Approach 1:
The patent introduces an intermediary system that sits between the machine-learning models and the data processes. This intermediary automatically generates data objects representing model implementations, tracks usage, and validates compliance with system requirements frameworks, thereby maintaining transparency without reducing productivity
Solution Approach 2:
The system implements feedback mechanisms by automatically generating data objects that capture model implementation details and usage information. This feedback loop enables continuous monitoring and validation of model compliance, ensuring transparency while maintaining automation efficiency
2Productivity
If conventional systems allow use of machine-learning models, then productivity is improved, but ability to control usage and verify inputs/outputs deteriorates
Solution Approach 1:
The system performs preliminary actions by generating data objects that represent machine-learning model implementations before the models are deployed. These data objects capture model details, datasets, and configuration information in advance, enabling subsequent validation and control measures to be applied effectively
Solution Approach 2:
An intermediary validation system is introduced that uses the pre-generated data objects to verify model inputs, outputs, and compliance with system requirements frameworks. This intermediary layer provides reliability control without interfering with the productive use of machine-learning models
3Ease of operation
If machine-learning models are implemented without centralized management, then ease of operation is improved, but compliance with system requirements deteriorates
Solution Approach 1:
The system implements self-service mechanisms where the machine-learning model implementation process automatically generates the necessary data objects and compliance documentation. The system serves itself by autonomously tracking usage, validating requirements, and generating reports, maintaining ease of operation while ensuring precision compliance
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for managing implementation of machine-learning models within computing environments according to system requirements frameworks via common data objects. The disclosed system generates a common data object to represent an implementation of a machine-learning model with a data process. For example, the disclosed system determines attribute values of the common data object according to data objects representing the machine-learning model and related datasets. Furthermore, the disclosed system utilizes the common data object to validate the machine-learning model according to a digital representation of a system requirements framework that includes usage requirements for machine-learning models to store, process, transmit, or otherwise handle specific data types in specific ways for the one or more data processes within a computing environment. The disclosed systems also perform operations to implement, suspend, or otherwise modify the machine-learning model or datasets based on the validation.