Automated ML Model Validation Flow for Custom Goals
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Solution Overview
Problem
Conventional machine learning model training and validation techniques fail to align with specific application needs and business goals, leading to a gap between machine learning models and real-world applications, and do not effectively balance quality pillars such as accuracy, runtime, and security.
Innovation Solution
A system and method that automatically detect custom goals of a machine learning application, determine the relative importance of quality pillar performance categories, and generate automated machine learning model tests to perform validation testing, dynamically adjusting the validation flow to prioritize specific performance categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standardized training and validating modules are used to streamline the training and validating steps, then the validation process becomes more efficient and consistent, but the validation cannot align with specific application needs and business goals
Solution Approach 1:
The validation framework dynamically adapts to different machine learning applications by automatically detecting custom goals and determining relative importance of performance categories. The system generates customized validation tests based on the specific application context, allowing the validation process to flex and adjust rather than following a rigid standardized protocol.
Solution Approach 2:
The system changes validation parameters by detecting custom goals and calculating relative importance weights for different performance categories. Instead of using fixed validation parameters, the system adjusts parameters such as test selection, data sampling, and evaluation metrics based on the detected application-specific requirements and business goals.
2Device complexity
If conventional validation techniques are used, then the validation process is simple and standardized, but it fails to balance quality pillars such as accuracy, runtime, and security
Solution Approach 1:
The validation framework segments the validation process into distinct performance categories (accuracy, runtime, security, etc.) and assigns relative importance weights to each. This segmentation allows the system to independently evaluate and balance different quality pillars while maintaining an organized, manageable validation structure that doesn't become overly complex.
Solution Approach 2:
The system incorporates feedback mechanisms where the detected custom goals and performance category importance directly influence the validation test generation. The feedback loop ensures that validation tests are continuously aligned with application needs, automatically adjusting the balance between different quality pillars based on real-time detection of business goals.
3Adaptability or versatility
If automated machine learning model tests are generated based on detected custom goals, then the validation aligns with application-specific needs, but the system complexity increases
Solution Approach 1:
The validation system performs self-service by automatically detecting custom goals and generating validation tests without requiring extensive manual configuration. The system autonomously analyzes application requirements, determines performance category importance, and creates customized validation workflows, reducing the need for complex manual setup while maintaining high adaptability.
Solution Approach 2:
The framework provides universal functionality by handling multiple validation tasks within a single integrated system. It can detect various types of custom goals, evaluate different performance categories, generate diverse validation tests, and adapt to different machine learning applications all through one unified platform, avoiding the need for multiple separate complex tools.
Data Source
AI summary
In one aspect, a computer-implemented method includes detecting, by one or more processing devices, custom goals of a specified machine learning application; determining, by the one or more processing devices, relative importance of a plurality of performance categories for the specified machine learning application, based on the custom goals of the specified machine learning application; generating, by the one or more processing devices, automated machine learning model tests based on the determined relative importance of the plurality of performance categories for the specified machine learning application; and performing, by the one or more processing devices, validation testing of the machine learning model based on the automated machine learning model tests.