Regression Forecasting Engine for ML Model KPI Prediction
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Solution Overview
Problem
Existing technologies lack an efficient method to predict the performance of new machine learning (ML) models on key performance indicators (KPIs) before deploying them in production, leading to potential risks and suboptimal performance.
Innovation Solution
A computer-implemented method and system that uses a regression machine learning model to ingest proposed adjustments to an ML model, calculate value components for KPIs, and determine whether the results exceed a performance threshold, thereby recommending the adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a new machine learning model is deployed in production without prior performance prediction, then deployment speed is improved, but reliability of performance outcomes deteriorates
Solution Approach 1:
The system performs preliminary performance prediction using a regression ML model before deploying the new ML model to production. The forecasting engine calculates predicted KPI values and compares them against thresholds to determine whether the new model meets performance requirements, thereby ensuring reliable performance outcomes while maintaining deployment efficiency.
2Reliability
If performance prediction analysis is performed before deployment, then reliability of performance outcomes is improved, but time required for model deployment increases
Solution Approach 1:
The system performs a focused performance prediction analysis on key performance indicators using a regression model, rather than comprehensive testing. The forecasting engine calculates predicted KPI values and compares them against predefined thresholds, providing sufficient reliability assurance while minimizing the time overhead through targeted rather than exhaustive evaluation.
3Measurement precision
If manual evaluation of ML model performance is performed, then measurement precision is improved, but productivity of the deployment process deteriorates
Solution Approach 1:
The system uses an automated forecasting engine that self-evaluates the performance of new ML models by calculating predicted KPI values using a regression model. The system automatically compares predicted values against thresholds and generates deployment recommendations without requiring manual intervention, thereby maintaining precise performance measurement while significantly improving deployment productivity.
4Ease of operation
If no performance prediction is performed, then ease of operation is improved, but loss of information about model performance deteriorates
Solution Approach 1:
The system provides automated feedback about the expected performance of new ML models before deployment. The forecasting engine calculates predicted KPI values and compares them against thresholds, then returns a clear recommendation on whether the model meets performance requirements. This feedback mechanism maintains operational simplicity by providing actionable insights without requiring complex manual analysis.
Data Source
AI summary
A computer-implemented method and computer program product for predicting an impact of an adjustment to a machine learning model to key performance indicators, and a forecasting engine. The computer-implemented method may comprise receiving a proposed adjustment to a machine learning model, calculating, using a regression machine learning model to ingest the proposed adjustment, a set of value components for a key performance indicator (KPI), calculating a plurality of results for the KPI using the set of value components, automatically determining whether the plurality of results exceeds a performance threshold, and recommending the proposed adjustment based on the determination.