ML Model Deployment via Fitness Function for IoT Latency
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Solution Overview
Problem
Existing methods for deploying machine learning-based models to IoT devices face challenges in balancing prediction accuracy and response time due to computational complexity and resource constraints, leading to inefficiencies and suboptimal performance.
Innovation Solution
The system optimizes this by selecting and deploying prediction models tailored to specific IoT device profiles, using a multi-layered execution environment where the best-fitting models are placed on the device or cloud-based environments to minimize latency and maximize accuracy, with a fitness function determining the optimal model deployment based on processing power, memory, and other parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning-based models are deployed to IoT devices, then prediction accuracy is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent segments the model deployment architecture into multiple layers: edge devices (IoT devices) and cloud-based execution environments. This segmentation allows complex models to be distributed across different computational layers, reducing the complexity burden on individual IoT devices while maintaining overall prediction accuracy through collaborative inference.
Solution Approach 2:
The patent creates a universal model deployment framework that can adapt to various IoT device types and cloud environments. The system uses a fitness function that evaluates multiple candidate models against device profiles, enabling the same deployment mechanism to serve diverse device configurations with different computational capabilities, thereby managing complexity through standardized multi-functional architecture.
2Measurement precision
If complex prediction models are deployed to meet operational requirements, then prediction accuracy is improved, but response time deteriorates due to computational complexity
Solution Approach 1:
The patent implements a dynamic model selection mechanism where the fitness function evaluates candidate models based on real-time device profiles and operational requirements. This dynamic approach allows the system to select the most appropriate model for each specific context, balancing accuracy and response time by choosing models that are optimally suited to the current device capabilities and performance requirements rather than using a fixed complex model in all scenarios.
Solution Approach 2:
The patent changes the parameters of model selection by using a fitness function that considers multiple factors including device processing power, memory constraints, and operational requirements. By adjusting these selection parameters dynamically, the system can optimize the trade-off between model complexity (affecting accuracy) and computational speed (affecting response time) for each specific deployment scenario.
3Productivity
If machine learning models are deployed to IoT devices, then operational efficiency is improved, but resource constraints are exceeded
Solution Approach 1:
The patent applies local quality by tailoring model deployment to specific device characteristics. The fitness function evaluates candidate models against individual device profiles, selecting models that are locally optimized for each device's specific resource constraints (processing power, memory, energy). This ensures that each device receives a model appropriate to its local capabilities, improving operational efficiency without exceeding resource constraints.
4Measurement precision
If multiple candidate models are evaluated for deployment, then model selection accuracy is improved, but computational overhead and time increase
Solution Approach 1:
The patent implements partial action by evaluating multiple candidate models but selecting only the single best-fitting model for deployment. The fitness function performs comprehensive evaluation of candidate models against device profiles, but the system is designed to choose just one optimal model rather than deploying multiple models or performing exhaustive searches. This partial evaluation approach achieves sufficient model selection accuracy while limiting the time and computational overhead to practical levels.
Data Source
AI summary
A method of deploying machine learning-based models may include identifying a profile of a target execution environment to implement a machine learning-based model in communication with a cloud infrastructure. The method may further include identifying, using the profile, a software module implementing the model. The method may further include causing the software module to be uploaded from a code repository associated with the cloud infrastructure to the target execution environment.