Automated Deep Learning Model Generation System
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Solution Overview
Problem
Junior developers face significant challenges in generating deep learning models due to the need for extensive knowledge in training data preparation, model selection, and parameter settings, which delays development progress and is not user-friendly.
Innovation Solution
A method and apparatus for generating a target deep learning model by obtaining user instructions and original data, determining a suitable deep learning model, training it with generated data, and converting it into a desired format, allowing users to specify task requirements without needing detailed model knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers manually prepare training data, select models, and configure parameters according to personal experience, then model customization and performance are improved, but development time and complexity increase significantly
Solution Approach 1:
The system enables self-service automated model generation where the platform automatically prepares training data, selects appropriate models, and configures parameters based on user requirements without manual intervention. This resolves the contradiction by eliminating time-consuming manual operations while maintaining model performance through algorithmic optimization.
Solution Approach 2:
The system performs preliminary actions by pre-processing training data, pre-selecting candidate models, and pre-configuring parameters before the user actually needs them. This automation of preparatory work reduces development time while ensuring model reliability through systematic approaches.
2Reliability
If developers need extensive knowledge in training data preparation, model selection, and parameter settings, then model quality is improved, but ease of use deteriorates
Solution Approach 1:
The automated model generation system acts as an intermediary between the user and the complex deep learning process. It translates simple user requirements into sophisticated model configurations, shielding users from technical complexity while ensuring model quality through expert-level automated decisions.
Solution Approach 2:
The system segments the complex model generation process into automated discrete steps: data preparation, model selection, parameter configuration, and training. This segmentation allows each step to be optimized independently by algorithms while presenting a simplified interface to users.
3Adaptability or versatility
If developers write repeated code and manually fine-tune models, then model customization is improved, but productivity decreases
Solution Approach 1:
The system uses template-based model configurations and pre-trained models as copies that can be automatically adapted to different tasks. This eliminates repeated code writing while maintaining customization capabilities through parameter adjustment rather than manual coding.
Solution Approach 2:
The system achieves model customization through automated parameter changes rather than manual code modifications. By adjusting parameters and selecting from pre-defined model architectures, the system maintains versatility while dramatically improving productivity through automation.
Data Source
AI summary
A method and an apparatus for generating a target deep learning model are provided. In the method, an instruction and original data for generating the target deep learning model is obtained from a user. The instruction includes a task expected to be performed by the target deep learning model. Then, training data is generated from the original data. A first deep learning model corresponding to the task is determined. Then, the first deep learning model is trained with the training data to obtain the target deep learning model.