Model Management Server for Neural Network Deployment
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Solution Overview
Problem
Users face difficulties in performing machine learning with neural networks (NNs) effectively and deploying them for practical use, as existing systems lack streamlined processes for preparing and deploying NN models.
Innovation Solution
A computer system is designed to support users by preparing learning models, inputting and processing data, generating learned models, determining practical models, and executing inference using these models, with features like data annotation and model deployment across a network, facilitating the machine learning and inference processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users perform machine learning and deployment of neural networks manually, then they can customize models according to specific purposes, but the process becomes complex and difficult to operate
Solution Approach 1:
The patent introduces a model management server as an intermediary between users and the machine learning infrastructure. This server provides automated model training, selection, and deployment services, shielding users from the complexity of manual neural network configuration while enabling purpose-specific model customization through simplified interfaces.
Solution Approach 2:
The system implements self-service capabilities where the model management server automatically performs model training, evaluation, and deployment without requiring user intervention in complex technical processes. Users simply specify their needs, and the system autonomously handles the machine learning workflow, reducing operational difficulty while maintaining customization.
2Adaptability or versatility
If the system provides comprehensive machine learning support, then users can easily deploy models, but the system complexity increases
Solution Approach 1:
The model management server is designed as a universal platform that handles multiple machine learning tasks (training, selection, deployment, updating) through a unified architecture. This multi-functional design provides comprehensive model deployment support across different scenarios without requiring separate specialized systems for each function, managing system complexity through consolidation.
Solution Approach 2:
The system segments machine learning functionality into distinct modular components (model training module, model selection module, deployment module) that can be independently managed and scaled. This segmentation allows the system to provide versatile support while keeping each component's complexity manageable and enabling selective activation based on user needs.
3Reliability
If multiple models are trained and stored, then better practical models can be selected, but storage and processing requirements increase
Solution Approach 1:
The system performs preliminary model training and evaluation before deployment, pre-processing multiple candidate models and storing only those that meet quality criteria. This preliminary action ensures high reliability of deployed models while avoiding the need to store and process all possible model variations, reducing the quantity of stored models through advance filtering.
Solution Approach 2:
The model management server dynamically adjusts model selection criteria and training parameters based on specific deployment scenarios and performance requirements. By changing parameters such as accuracy thresholds, resource constraints, and application-specific metrics, the system optimizes the balance between model quality and the number of models required, storing only what is necessary for reliable deployment.
Data Source
AI summary
The purpose of the present invention is to train a learning model and thereby create a utility model, and assist with an operation for making practical use thereof. Provided is a computer system 50 for preparing learning models in one or more learning units 102; receiving an input of learning data from a data source 111, and training said one or more learning models using the learning data. One or more utility models are finalized on the basis of said one or more trained learning models, and said one or more utility models are deployed to one or more inference units 114. Each of said one or more inference units 114: receives an input of utility data from the data source 111; provides the utility data to the utility models and executes an inference; and transmits, to a data target 113, inference result data outputted from the utility models.