Knowledge Graph Automated Model Retraining
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Solution Overview
Problem
Traditional processes for retraining analytical models are expensive, time-consuming, and dependent on isolated domain knowledge, requiring manual updates of datasets and parameters, which leads to inefficiencies in maintaining model performance over time.
Innovation Solution
A model retraining tool (MR tool) utilizes a knowledge graph (KG) to automate the retraining process by capturing data and metadata, inferring feature weights, and providing insights for improving model performance, thereby reducing resource costs and time through structured data presentation and semantic modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual processes are used for retraining analytical models, then domain expertise can be applied, but the process becomes expensive and time-consuming
Solution Approach 1:
The system enables automated self-service retraining by allowing analytical models to automatically identify their own retraining needs, select appropriate datasets, and execute retraining workflows without manual intervention. The model performance monitoring component continuously tracks model drift and automatically triggers retraining when performance degradation is detected, eliminating the need for manual monitoring and intervention while maintaining model reliability.
Solution Approach 2:
The patent introduces an automated retraining management system that acts as an intermediary between domain experts and analytical models. This intermediary layer captures and structures domain knowledge into reusable components, manages dataset selection and preparation, coordinates retraining workflows, and handles model versioning. This intermediary automates the previously manual processes while preserving the application of domain expertise through structured knowledge bases and automated decision-making rules.
2Reliability
If traditional manual processes are used for retraining analytical models, then model updates can be performed, but extensive manual intervention is required
Solution Approach 1:
The system implements self-service automation where the analytical model autonomously monitors its own performance, detects when retraining is needed based on performance degradation thresholds, automatically selects relevant datasets from available data sources, and executes the retraining process. This eliminates extensive manual intervention while ensuring model performance is maintained through continuous automated monitoring and retraining.
Solution Approach 2:
The patent implements a feedback-driven automated retraining system where model performance metrics are continuously monitored and fed back to the retraining management system. When performance degradation exceeds predefined thresholds, the feedback loop automatically triggers retraining workflows. The system also provides feedback on retraining outcomes to continuously improve the automated decision-making process, reducing manual intervention while maintaining model reliability.
3Productivity
If automated retraining is implemented, then efficiency improves, but complexity of the system increases
Solution Approach 1:
The patent segments the automated retraining system into distinct modular components: model performance monitoring modules that track specific metrics, dataset management modules that handle data selection and preparation, retraining workflow engines that coordinate the retraining process, and model versioning systems that manage model lifecycles. Each module operates independently with well-defined interfaces, enabling high automation efficiency while managing system complexity through modular architecture that allows independent development, testing, and maintenance of each component.
Solution Approach 2:
The patent implements a universal automated retraining management system that can handle multiple analytical models across different domains and applications through a single unified platform. The system provides multi-functional capabilities including performance monitoring, dataset management, workflow coordination, and version control that can be applied to various model types and retraining scenarios. This universal approach improves productivity by consolidating retraining operations while managing complexity through standardized interfaces and reusable components that can be applied across different contexts.
Data Source
AI summary
A model retraining tool is provided for utilizing a knowledge graph to retrain analytical models used in production. The model retraining tool retrains the analytical models to improve performance of the analytical models in an efficient and resource conserving manner.