Payload Data Exposure for Non-Integrated ML Systems
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Solution Overview
Problem
Current machine learning systems require time-consuming and labor-intensive integration with scoring engines to log payload data, making it difficult to expose inputs and outputs for analysis, especially in non-integrated systems.
Innovation Solution
A method is introduced to simulate integration by generating a generic binding identifier for machine learning systems and a generic subscription identifier for deployed models, allowing payload data including user requests and responses to be stored in a database for analysis without actual integration, using a payload management engine that includes modules for binding, subscription, reception, logging, and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If integration with scoring engines is performed to log payload data, then payload data can be exposed for analysis, but integration becomes time-consuming and labor-intensive
Solution Approach 1:
The patent creates a copy of the payload data that includes the input data, output data, and metadata associated with machine learning model inferences. This copied payload data is stored in a database table without requiring actual integration with scoring engines, thereby exposing payload data for analysis while avoiding time-consuming integration processes
Solution Approach 2:
The patent introduces a payload data structure as an intermediary layer between the machine learning system and the database. This payload data structure acts as a mediator that captures and stores inference information without requiring direct integration with scoring engines, enabling payload exposure while eliminating integration complexity
2Loss of information
If integration with scoring engines is performed to log payload data, then payload data can be exposed for analysis, but manual burden increases
Solution Approach 1:
The patent implements a self-service mechanism where the payload data structure automatically captures and stores inference information from machine learning models. The system autonomously logs payload data including input data, output data, and metadata without requiring manual integration efforts or analyst intervention, thereby reducing manual burden while exposing payload data for analysis
3Measurement precision
If individual integrations are performed for each scoring engine, then specific model logging is enabled, but device complexity increases
Solution Approach 1:
The patent creates a universal payload data structure that can log inference information for multiple machine learning models and scoring engines through a single unified mechanism. This universal structure includes fields for input data, output data, model identifiers, and metadata, enabling precise logging across diverse models without requiring individual integrations for each scoring engine, thereby reducing integration complexity while maintaining logging precision
Data Source
AI summary
Aspects of the present invention provide an approach for exposing payloads from non-integrated machine learning systems. A generic binding identifier is established to represent a machine learning (ML) system among a set of non-integrated learning systems. A generic subscription identifier is established to represent a deployed model in the ML system. Payload data including a user request, a response, the generic binding identifier, and the generic subscription identifier are received from the ML system and stored in a database for later analysis to identify any issues related to the deployed model.