Auditing System for Machine Learning Model Migration Evaluation
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Solution Overview
Problem
Conventional methods for evaluating the impact of changes to machine learning models are inefficient, relying on manual determination of descriptive statistics, which can be time-consuming and prone to errors, and do not effectively adapt to changes in the computing environment, leading to potential network downtime and operator intervention.
Innovation Solution
An auditing system that compares the output of a machine learning model on one computing platform to its updated version on another platform, generating performance reports and classifying the updated model as 'pass' or 'fail' based on predefined criteria, allowing for dynamic updating of the computing environment and modification of model parameters to ensure smooth migration and operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of descriptive statistics is used to evaluate model changes, then evaluation can be performed, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with an automated machine learning system. The auditing system automatically executes both the original and updated models, collects output data, and performs comparative analysis without human intervention, thereby eliminating time-consuming manual operations while maintaining or improving evaluation accuracy through systematic automated procedures
Solution Approach 2:
The auditing system performs self-evaluation by automatically comparing its own model outputs before and after updates. The system independently executes models, gathers performance data, calculates metrics, and generates evaluation reports without requiring external manual analysis, enabling the system to self-assess the impact of its own changes
2Productivity
If model changes are implemented without automated evaluation, then model updates can be deployed, but network downtime and operator intervention are required
Solution Approach 1:
The patent implements preliminary evaluation before model deployment by automatically executing both the original and updated models, comparing their outputs, and assessing performance metrics in advance. This preliminary automated assessment determines whether the updated model meets acceptance criteria before actual deployment, preventing unnecessary downtime and ensuring smooth transitions without requiring operator intervention during deployment
Solution Approach 2:
The auditing system establishes a feedback loop that automatically monitors model performance changes, compares metrics against predefined thresholds, and provides actionable insights. This continuous feedback mechanism enables automated decision-making about model deployment, maintaining system availability by quickly identifying and resolving potential issues without human intervention
Data Source
AI summary
An auditing system executes a first machine learning model on a first computing platform using input data to generate first output data. The auditing system executes a second machine learning model on a second computing platform using the input data to generate second output data. The second machine learning model is generated by migrating the first machine learning model to the second computing platform. The auditing system determines one or more performance metrics based on comparing the first output data to the second output data. The auditing system classifies, based on the one or more performance metrics, the second machine learning model with a classification. The classification comprises a passing classification or a failing classification. The auditing system causes the second model to be modified responsive to classifying the second model with a failing classification.