Application-Generating Code Evaluation With Influence Factors
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Solution Overview
Problem
Continuous integration/continuous delivery (CI/CD) in machine learning development faces challenges due to the statistical nature of model performance evaluation, necessitating the use of proxies like shadow deployment or test sets, which are costly and uncertain, and requires accurate estimation of code changes' impact on performance metrics across multiple metrics and customer groups.
Innovation Solution
A system evaluates changes to application-generating code by comparing performance metrics of candidate and stable applications using a statistical model with an influence factor, fitting performance distributions to quantify statistically significant improvements or deteriorations, and implements modifications only if they meet predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shadow deployment or test sets are used to evaluate model performance, then model performance can be assessed, but cost increases and statistical uncertainty remains
Solution Approach 1:
The system implements continuous feedback loops where performance metrics are collected from production data, analyzed through statistical models, and used to iteratively refine the evaluation process. This feedback mechanism reduces statistical uncertainty by accumulating evidence over time and adjusting evaluations based on actual production performance rather than relying solely on static test sets.
Solution Approach 2:
The patent introduces statistical models and influence factors as intermediaries between the code changes and performance evaluation. These intermediaries process and interpret the relationship between code modifications and performance outcomes, providing a more reliable assessment that accounts for statistical variability and reduces uncertainty in performance predictions.
2Productivity
If code changes are frequently integrated and deployed, then developer productivity increases, but statistical uncertainty in performance evaluation worsens
Solution Approach 1:
The system performs preliminary statistical analysis and influence factor calculation before deploying code changes to production. By pre-evaluating the potential impact of changes using statistical models on historical data, the system enables faster decision-making that supports frequent integration while maintaining evaluation reliability through pre-computed statistical evidence.
Solution Approach 2:
The patent dynamically adjusts evaluation parameters and confidence thresholds based on the amount of available data and the nature of code changes. This adaptive parameter adjustment allows the system to maintain statistical reliability even with frequent deployments by modifying the stringency of evaluation criteria according to the statistical confidence available at each evaluation point.
3Adaptability or versatility
If multiple performance metrics and customer groups are evaluated, then evaluation comprehensiveness improves, but complexity of the evaluation process increases
Solution Approach 1:
The system implements a universal statistical evaluation framework that handles multiple performance metrics and customer groups through a single unified model structure. This multi-functional approach allows the same statistical machinery to evaluate diverse metrics (accuracy, latency, resource usage) and multiple customer segments simultaneously, reducing overall system complexity while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The patent segments the evaluation process into modular components that can independently assess different metrics and customer groups. By dividing the comprehensive evaluation into separate, manageable statistical analyses for each metric and segment, then aggregating the results through influence factors, the system achieves thorough evaluation without overwhelming complexity in the evaluation workflow.
Data Source
AI summary
Techniques for modifying application-generating code are disclosed. A system determines if a change to an application-generating code module results in a statistically significant change in the performance of applications generated by the module. The system makes the determination by comparing performance metrics of multiple applications generated by an un-changed application-generating code module to performance metrics of multiple applications generated by a changed application-generating code module. The system compares the performance metrics by generating distributions representing the respective performance metrics. The system generates the distributions by fitting the performance metrics to a statistical algorithm including an inter-application influence factor. The influence factor modifies the statistical model by converting a change in the shape of the distribution from (a) around a center of the performance metric distribution to (b) a shift of the center of the performance metric distribution toward a clustering of measured performance metric values.