Deep Learning Model Update Automation
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Solution Overview
Problem
The manual triggering of training and updating processes for deep learning models leads to low efficiency in updating these models.
Innovation Solution
A method and apparatus for automatically updating deep learning models by acquiring a training dataset, training the model, and incrementing the number of training iterations, with the process stopping when a threshold is reached or continuing after a preset time interval if the threshold is not met, ensuring regular updates and improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual triggering is used for training and updating deep learning models, then the process can be controlled by users, but the updating efficiency is low
Solution Approach 1:
The system automatically monitors data changes and triggers model retraining without manual intervention. The training process serves itself by automatically detecting when new data arrives and initiating the appropriate training workflow, eliminating the need for user-triggered updates while maintaining control through automated decision-making
Solution Approach 2:
The system implements a feedback mechanism where data changes are continuously monitored and fed back to the training module. When changes exceed a threshold or new data arrives, the system automatically triggers retraining, creating a closed-loop control system that responds to actual data state rather than manual commands
2Reliability
If frequent training iterations are performed to improve model accuracy, then model performance improves, but training time and computational resources increase
Solution Approach 1:
The system performs partial training iterations based on data change magnitude rather than always executing full training cycles. When data changes are minor, it performs fewer iterations; when changes are significant, it performs more iterations. This selective approach achieves sufficient model accuracy while minimizing unnecessary training time and computational waste
Solution Approach 2:
The training iteration count is made dynamic rather than fixed. The system adjusts the number of training iterations based on real-time conditions such as data change magnitude, model performance metrics, and computational resource availability. This dynamic adjustment allows the system to optimize between accuracy and training time adaptively
Data Source
AI summary
The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.