Dual Machine Learning Model Latency Handling

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Solution Overview

Problem

Real-time machine learning systems face challenges in maintaining consistent processing due to latency fluctuations, leading to time-outs or errors when preferred models fail to complete processing within acceptable latency periods.

Innovation Solution

Implementing a dual-machine learning system with a preferred and a fallback model, where the fallback model generates responses within predetermined latency limits by processing approximated data, ensuring reliable and satisfactory outputs even if the preferred model exceeds latency constraints.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a preferred machine learning model is used for real-time processing, then model accuracy and quality are improved, but processing latency increases and may exceed acceptable time limits

Engineering Contradiction:

Improvemodel accuracyVSAvoidprocessing latency

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system changes the complexity parameter of the machine learning model based on timing conditions. When the preferred model exceeds latency thresholds, the system switches to a fallback model with reduced complexity parameters, thereby reducing processing time while maintaining acceptable output quality for real-time responses.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system dynamically selects between different model configurations based on real-time processing conditions. The model complexity is not fixed but adapts according to whether the preferred model can complete processing within the latency budget, allowing the system to optimize between accuracy and speed dynamically.

Inventive Principle:

Principle #15Dynamics

2Loss of time

If a fallback machine learning model is used for real-time processing, then processing latency is reduced and meets deadline constraints, but model accuracy and output quality decrease

Engineering Contradiction:

Improveprocessing latencyVSAvoidmodel accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The fallback model performs a simplified version of the processing task with reduced computational action. Instead of full model execution, it provides partial processing that is sufficient to meet latency requirements and generate timely responses, even if the output quality is lower than the preferred model.

Inventive Principle:

Principle #16Partial or excessive action

3Manufacturing precision

If the preferred model processing time is extended beyond acceptable latency period, then more complete processing is achieved, but real-time response requirements are violated causing time-outs or errors

Engineering Contradiction:

Improveprocessing completenessVSAvoidreal-time response reliability

Core Design Contradiction:

Manufacturing precisionVSReliability

Solution Approach 1:

The system prepares a fallback model in advance as a pre-configured alternative. When the preferred model is running and may exceed latency limits, the fallback model is already positioned to take over, ensuring that a complete response can be generated within the latency budget without causing time-outs or errors.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system cushions against the risk of preferred model latency by having a fallback model ready. This prior preparation ensures that even if the preferred model takes too long, the system can switch to the fallback model to prevent time-outs and maintain real-time response reliability.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

4Reliability

If a dual-machine learning system with preferred and fallback models is implemented, then real-time response reliability is improved, but system complexity increases

Engineering Contradiction:

Improvereal-time response reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The fallback model acts as a simple, lightweight alternative that doesn't need to be as sophisticated as the preferred model. It provides sufficient functionality for real-time responses with reduced complexity, allowing the system to maintain reliability without requiring two equally complex models.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

Data Source

PatentUS11004010B2Processing real-time processing requests using machine learning models

Publication Date: 2021.05.11 ESENTIRE

AI summary

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing consistent processing in a machine learning system are disclosed. A real-time processing request may be received and processed by both a preferred machine learning model and a fallback machine learning model. Processing for the preferred machine learning model may include obtaining additional information. A determination may be made regarding whether the processing of the real-time request by the preferred machine learning model has completed as of an expiration of an acceptable latency period. If the preferred model has not completed as of the expiration of an acceptable latency period, the response to the real-time request may be generated from the fallback model output. If the preferred model has completed prior to or by the expiration of the acceptable latency period, the response to the request may be generated from the preferred model output.