Neural Network Deployment Wizard for Non-Expert Deep Learning
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Solution Overview
Problem
Small and medium-sized enterprises lack the professional knowledge to utilize neural network technology, hindering their ability to effectively deploy and utilize neural network models.
Innovation Solution
A neural network model deployment method and apparatus that provides a specification wizard for user input, trains neural networks based on user requirements, generates template code, optimizes and deploys the network to target devices, and manages resources, all without requiring specialized knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network technology is used, then deep learning service performance is improved, but professional knowledge requirements increase
Solution Approach 1:
The patent introduces an automated neural network deployment system that acts as an intermediary between the user and complex neural network technology. The system includes automated model selection, configuration, training, and deployment components that translate simple user requirements into complex technical operations without requiring users to have professional knowledge in neural networks
Solution Approach 2:
The deployment system performs self-service by automatically selecting appropriate neural network models, configuring training parameters, executing training processes, and deploying models to target devices based on user specifications. The system autonomously handles all technical complexities including hyperparameter tuning, model optimization, and resource allocation without human intervention in the technical decision-making process
2Measurement precision
If neural network model development is performed manually, then model accuracy can be optimized, but development time increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple neural network model templates with different architectures and parameters. When a user initiates deployment, the system automatically selects from these pre-prepared models and performs rapid configuration based on the specific requirements, eliminating the time-consuming manual model design and selection process while maintaining the ability to achieve accurate results
Solution Approach 2:
The deployment system automatically adjusts model parameters including architecture configuration, training hyperparameters, and optimization settings based on the target device specifications and performance requirements. This automated parameter tuning achieves model accuracy optimization without requiring manual intervention, significantly reducing development time while maintaining high model performance
Data Source
AI summary
Disclosed herein are a neural network model deployment method and apparatus for providing a deep learning service. The neural network model deployment method may include providing a specification wizard to a user, searching for and training a neural network based on a user requirement specification that is input through the specification wizard, generating a neural network template code based on the user requirement specification and the trained neural network, converting the trained neural network into a deployment neural network that is usable in a target device based on the user requirement specification, and deploying the deployment neural network to the target device.