ML Evaluation Pipeline Automation via Requirements Management
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Solution Overview
Problem
Current methods for evaluating machine learning models are inefficient and require human intervention, as each component of the evaluation process is separate and requires manual interpretation, leading to a slow and non-automated evaluation process.
Innovation Solution
A streamlined pipeline for evaluating machine learning models is introduced, where a requirements management layer interprets and transmits instructions to an execution layer for the evaluation process, with the results displayed through a user interface, encapsulating the entire process in a single pipeline for improved automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If separate systems are used for each component of the ML evaluation process, then each component can be independently developed and maintained, but the evaluation process becomes slow and requires extensive human intervention
Solution Approach 1:
The patent merges separate evaluation components (data acquisition, model execution, result analysis) into a unified automated pipeline system. This integration enables end-to-end automation of the ML model evaluation process, eliminating manual intervention between stages while maintaining modular architecture for independent component development.
Solution Approach 2:
The evaluation pipeline is designed as a universal system that can handle multiple ML models, various evaluation metrics, and different data sources through a single integrated framework. This multi-functional approach allows the system to automate diverse evaluation tasks without requiring separate specialized systems for each component.
2Productivity
If manual interpretation is required for each evaluation step, then human expertise can be applied to optimize the model, but the evaluation process becomes time-consuming and inefficient
Solution Approach 1:
The evaluation pipeline implements self-service automation where the system automatically executes models, collects results, analyzes performance metrics, and generates evaluation reports without requiring manual interpretation at each step. The automated analysis components process evaluation data and generate insights independently, significantly improving evaluation speed while maintaining thoroughness.
Solution Approach 2:
The system incorporates automated feedback mechanisms where evaluation results are automatically analyzed and fed back into the pipeline for iterative optimization. This feedback loop enables continuous automated improvement of model performance without requiring constant human intervention, thereby increasing productivity while simplifying operation.
3Adaptability or versatility
If multiple separate systems are used for data acquisition, model execution, and result analysis, then each system can be optimized independently, but the overall evaluation process lacks streamlining and automation
Solution Approach 1:
The evaluation pipeline is segmented into distinct modular components (data acquisition module, model execution module, result analysis module) that can be independently optimized and configured. Each segment maintains its flexibility and adaptability while being integrated into a unified automated workflow, allowing independent optimization without sacrificing overall process efficiency or increasing evaluation time.
Data Source
AI summary
Provided are a method, system, and device for evaluating a machine learning (ML) model. The method may include: receiving, by a requirements management layer, at least one requirement obtained from a storage layer; interpreting, by the requirements management layer, the at least one requirement; and transmitting, by the requirements management layer, instructions to perform an ML evaluation process to an execution layer based on the interpreted requirements, wherein the execution layer transmits an output signal with the results of the ML evaluation process upon completing the ML evaluation process.