Model Performance Aggregation for Low-Noise ML Pipeline Monitoring
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Solution Overview
Problem
Existing machine-learning data models face challenges in efficiently evaluating and aggregating performance measures to ensure predictive accuracy and stability, particularly in dynamic or chaotic systems, due to issues like computational instability, data drift, and biased predictions.
Innovation Solution
A system and method for aggregating computationally generated performance reports of machine-learning models, using reporting jobs that monitor and evaluate multiple sub-models, and generate aggregated alerts based on predefined conditions, reducing unnecessary alerts by validating model baselines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If performance measures are computed and distributed for each individual sub-model, then model performance monitoring coverage is improved, but alert noise and computational overhead increase
Solution Approach 1:
The patent combines performance measures from multiple sub-models into a single aggregated performance measure. The aggregation job consolidates individual sub-model performance metrics and applies aggregation logic to produce one comprehensive performance indicator, thereby reducing alert noise while maintaining monitoring coverage.
Solution Approach 2:
The aggregation job serves as an intermediary between individual sub-model performance measurements and the final alerting system. It processes and consolidates performance data from multiple sources before generating alerts, filtering out minor variations and focusing on significant performance deviations.
2Object-generated harmful factors
If performance measures are aggregated from multiple sub-models, then alert noise is reduced, but computational complexity increases
Solution Approach 1:
The system segments the performance monitoring function into distinct components: individual sub-model performance measurement jobs and a separate aggregation job. This segmentation allows each component to focus on a specific task, simplifying the overall computational structure while achieving noise reduction through aggregation.
Solution Approach 2:
Performance measures are pre-computed for each sub-model before aggregation. This preliminary computation allows the aggregation job to work with pre-processed data, reducing its computational burden and enabling efficient consolidation of performance metrics from multiple sources.
3Measurement precision
If performance measures are computed frequently, then predictive accuracy monitoring is improved, but computational resource consumption increases
Solution Approach 1:
The system implements periodic performance measurement through scheduled reporting jobs. Individual sub-model performance is measured at regular intervals, and aggregation jobs consolidate these periodic measurements. This periodic approach balances monitoring precision with computational resource management by avoiding continuous computation.
Data Source
AI summary
Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for aggregating computationally generated performance reports a machine-learning data model pipeline for technological performance and/or predictive accuracy. The tool may acquire multiple performance measure reports that that quantitatively asses the performance of a model pipeline based on a configuration file that facilitates validation of the technological performance and predictive accuracy of the model. Additionally, when the conditions defined by the configuration file are met for of the performance measure reports the tool may broadcast a notification to a predetermined system.