Machine Learning Model Evaluation via Output Substitution
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Solution Overview
Problem
The existing process for testing machine learning models in application infrastructure is time-consuming and resource-intensive, requiring deployment and integration of the model, which leads to delays in development and deployment.
Innovation Solution
A system and method for evaluating machine learning models without deploying them in the application infrastructure, by generating test outputs based on input data, replacing deployed model outputs with test outputs in the data, and providing the modified data to the application infrastructure to obtain evaluation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are deployed in application infrastructure for testing, then evaluation accuracy is improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent creates a copy of the application infrastructure that includes a copy of the deployed machine learning model. This copy allows evaluation of the model without affecting the original deployment, enabling accurate assessment while avoiding the time and resource costs of deploying multiple copies to production infrastructure.
Solution Approach 2:
The patent introduces an intermediary evaluation system that sits between the original application infrastructure and the testing process. This intermediary handles the evaluation requests, manages the model copies, and returns results, thereby isolating the testing overhead from the production system and enabling efficient evaluation.
2Measurement precision
If machine learning models are deployed in application infrastructure for testing, then model performance evaluation is improved, but resource consumption increases
Solution Approach 1:
Instead of deploying multiple instances of the machine learning model to production infrastructure for testing purposes, the patent creates virtual or simulated copies within the evaluation system. This allows comprehensive performance evaluation using the same computational resources that would otherwise be dedicated to a single production instance.
Solution Approach 2:
The patent designs the evaluation system to serve multiple functions: it can evaluate different versions of the same model, test various input scenarios, and provide comprehensive performance metrics all within a single infrastructure setup. This multi-functionality reduces the need for separate testing infrastructures and optimizes resource utilization.
3Measurement precision
If traditional model testing processes are used, then thorough evaluation is achieved, but deployment speed decreases
Solution Approach 1:
The patent performs preliminary actions by creating and configuring model copies and evaluation environments before actual testing begins. This preparation work is done once in the evaluation system, allowing subsequent evaluations to proceed quickly without repeated setup overhead, thereby maintaining thoroughness while accelerating the overall process.
Solution Approach 2:
The patent enables parallel evaluation of multiple model versions and scenarios using copied infrastructure components. This parallel processing capability allows comprehensive evaluation to occur simultaneously rather than sequentially, significantly reducing the time required for thorough model assessment before deployment.
Data Source
AI summary
In some implementations, a device may obtain, via application infrastructure associated with an application, data associated with the application, the data including a first one or more outputs of a first machine learning model that is deployed via the application infrastructure. The device may generate a second one or more outputs of a second machine learning model. The device may replace the first one or more outputs with the second one or more outputs in the data to generate modified data. The device may provide, via the application infrastructure, the modified data to a processing component of the application. The device may obtain, based on providing the modified data, evaluation information indicating a performance level of the second machine learning model.