Automated AI Model Tuning via Genetic and Bayesian Optimization
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Solution Overview
Problem
Existing artificial intelligence model generation and optimization methods require significant manual intervention, leading to exposure of sensitive information and inefficiencies in scaling due to static inputs and parameters, which poses risks and resource constraints.
Innovation Solution
A computer-implemented method for generating and optimizing AI models with minimal manual intervention, using a data framework for validation and splitting, a deep framework for training and evaluation, and a tuner framework for automatic optimization of input features and hyperparameters through genetic algorithms and Bayesian optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used for model generation and optimization, then developers can access and adjust model parameters, but sensitive private information may be exposed and the process requires significant human resources
Solution Approach 1:
The system performs automatic model generation, tuning, and optimization without requiring manual developer intervention. The automated pipeline independently completes data validation, model training, hyperparameter optimization, and evaluation, eliminating the need for developers to access sensitive data while achieving the same model development goals
Solution Approach 2:
An automated orchestration layer acts as an intermediary between the model development process and potential data access points. This intermediary manages the entire pipeline automatically, controlling what information is accessed and when, thereby preventing unauthorized or unnecessary exposure of sensitive data while still enabling model development
2Ease of operation
If manual optimization is performed on artificial intelligence models, then developers can adjust parameters, but the process requires additional time and resources and cannot reach global optimal points
Solution Approach 1:
The patent replaces manual mechanical adjustment of model parameters with automated computational optimization algorithms. These algorithms systematically explore the parameter space using mathematical optimization techniques, eliminating the inefficiencies of manual trial-and-error approaches and enabling the system to reach global optimal points that would be inaccessible to human developers
Solution Approach 2:
The system implements dynamic, adaptive optimization that automatically adjusts its search strategy based on performance feedback. The automated pipeline continuously monitors model performance and iteratively refines parameters, enabling efficient navigation of complex parameter spaces that would be intractable for manual optimization while achieving superior model performance
3Device complexity
If static inputs and parameters are used in artificial intelligence models, then the model structure is simple, but the model cannot be efficiently modified or optimized for scaling
Solution Approach 1:
The patent transforms static model parameters into dynamic, adjustable variables that can be automatically optimized. The system implements a flexible architecture where inputs, features, and hyperparameters can be dynamically modified through automated tuning processes, enabling the model to adapt to different data sets and scaling requirements without requiring complex manual reconfiguration
Solution Approach 2:
The system systematically explores and modifies model parameters, input features, and configuration settings to optimize performance. By automatically adjusting these parameters based on performance metrics and feedback, the system enables efficient model scaling and adaptation while maintaining a relatively simple base architecture that can be tuned rather than fundamentally redesigned
Data Source
AI summary
A computer implemented method for generating and optimizing an artificial intelligence model, the method comprising receiving input data and labels, and performing data validation to generate a configuration file, and splitting the data to generate split data for training and evaluation; performing training and evaluation of the split data to determine an error level, and based on the error level, performing an action, wherein the action comprises at least one of modifying the configuration file and tuning the artificial intelligence model automatically; generating the artificial intelligence model based on the training, the evaluation and the tuning; and serving the model for production.