Client-Specific ML Model Validation Using Segmented Test Data

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Solution Overview

Problem

When updating machine learning models in network-based services, there is a risk of performance regression for certain clients due to the use of training data that may not be fully representative of their specific needs, leading to inconsistent model performance across diverse client bases.

Innovation Solution

Implementing client-specific validation using client-supplied test data to evaluate the performance of updated machine learning models on an individual basis, allowing for informed decisions on migration to new versions and minimizing the need for reverting to older versions or wasting resources on unsuccessful model launches.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If machine learning models are updated using general training data, then model improvements can be achieved, but performance regression may occur for certain clients due to lack of representativeness

Engineering Contradiction:

Improvemodel update efficiencyVSAvoidmodel performance consistency

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The validation process is segmented into client-specific groups based on shared characteristics (industry, data characteristics, performance metrics). Instead of treating all clients uniformly, the system divides them into segments that can be validated together using representative test data from each segment, resolving the contradiction between efficient general updates and reliable client-specific performance.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes the parameter of test data selection from generic/random to client-specific and representative. By selecting test data that matches client characteristics (industry type, data patterns, performance thresholds), the validation process ensures that model updates maintain reliability for each client segment while preserving overall update efficiency.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If client-specific validation is implemented, then model performance reliability is improved, but validation time and computational resources increase

Engineering Contradiction:

Improvemodel performance validation accuracyVSAvoidvalidation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

Clients with similar characteristics are grouped into segments that share common test data sets and validation criteria. By merging validation efforts for clients within the same segment, the system reduces redundant validation time while maintaining client-specific reliability through targeted representative testing.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system performs preliminary grouping and segmentation of clients before validation based on their characteristics and requirements. This preliminary action allows the validation process to proceed efficiently by pre-identifying which test data sets and validation criteria apply to each client segment, reducing overall validation time while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

3Speed

If model updates are deployed without validation, then deployment speed is improved, but resource waste occurs on unsuccessful model launches

Engineering Contradiction:

Improvedeployment speedVSAvoidcomputational resource waste

Core Design Contradiction:

SpeedVSLoss of energy

Solution Approach 1:

Validation is performed as a preliminary action before model deployment. By validating models using client-specific representative test data before actual deployment, the system identifies potential performance regressions early, preventing wasted computational resources on unsuccessful model launches while maintaining efficient deployment speeds for validated models.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The validation process provides feedback on model performance for each client segment before deployment. This feedback mechanism allows the system to approve or reject model updates based on predicted performance, preventing resource waste on models that would fail and enabling confident, efficient deployment of validated models.

Inventive Principle:

Principle #23Feedback

4Ease of operation

If general test data is used for validation, then validation process simplicity is improved, but client-specific performance issues are not detected

Engineering Contradiction:

Improvevalidation process simplicityVSAvoidperformance regression detection accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The validation system applies local quality by selecting test data that is specific to each client segment's characteristics (industry, data patterns, performance requirements). Instead of using uniform generic test data for all clients, the system tailors test data selection to local client needs, improving detection accuracy while maintaining operational simplicity through automated segmentation and data matching.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11610143B1Client-specific validation for migrating to a version of a machine learning model using client-supplied test data

Publication Date: 2023.03.21 AMAZON TECH INC

AI summary

A network-based service may provide a machine learning model for different clients. The network-based service may implement an interface that allows a client to identify a test data set for validating versions of the machine learning model specifically for the client. When a new version of the machine learning model is created, a validation test using the test data set identified by the client may be used. Results of the validation test may be used to make a decision regard whether to migrate workloads for the client to the new version of the machine learning model.