Pre-training Service System for AI Model Management
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Solution Overview
Problem
The high threshold for using large-scale pre-training models in AI technologies limits their accessibility and application, and there is a need to prevent abuse of these models, while also reducing the complexity of their usage for various industries.
Innovation Solution
A pre-training service system comprising a producer service module for generating pre-training models, an optimizer service module for optimizing models based on fine-tuning datasets, and a consumer service module for providing model prediction results, which includes features like activation checkpointing, mixed-precision training, sparse expert parallelism, and multi-modality training to enhance efficiency and versatility, along with obfuscation techniques to prevent model abuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-training models are made more accessible to reduce usage threshold, then ease of operation is improved, but model security and abuse prevention become more difficult to maintain
Solution Approach 1:
The patent introduces a service system as an intermediary between users and pre-training models. This system provides standardized interfaces (APIs) that simplify model usage while maintaining security controls. The intermediary layer enables easy operation through simple calls while preventing abuse through authentication, rate limiting, and monitoring mechanisms built into the service framework.
2Adaptability or versatility
If pre-training models are deployed across multiple industries to increase versatility, then adaptability is improved, but the complexity of managing and optimizing models increases
Solution Approach 1:
The patent designs a universal pre-training model service system that can serve multiple industries through a common framework. The system provides industry-specific models (finance, healthcare, manufacturing) within a unified architecture, allowing users to access different models through the same interface. This multi-functionality approach maintains versatility while managing complexity through standardized deployment and optimization processes.
Solution Approach 2:
The system segments the model management complexity by separating different functional components: model training, optimization, deployment, and monitoring are handled as independent modules. This segmentation allows each component to be optimized separately while maintaining overall system versatility across industries.
3Manufacturing precision
If comprehensive training is provided to improve model capabilities, then manufacturing precision is improved, but the time required for training and deployment increases
Solution Approach 1:
The patent implements preliminary action by pre-training models in advance with comprehensive capabilities before they are needed for specific applications. Industry-specific models are trained and optimized beforehand, allowing users to deploy ready-to-use models without time-consuming training. The system also provides pre-optimized versions of models that balance capability with training time requirements.
Data Source
AI summary
A pre-training service system is provided. The pre-training service system includes: a producer service module configured to provide a model producer with a model pre-training process for a pre-training dataset and generate a corresponding pre-training model; an optimizer service module configured to optimize the pre-training model according to a fine-tuning dataset provided by a model optimizer and obtain an optimized model; and a consumer service module configured to provide a model consumer with a service interface for the pre-training model or the optimized model, wherein the pre-training model or the optimized model is configured to perform inference on data provided by the model consumer and output a model prediction result.