AI Model Optimization via Automated Feature and Hyperparameter Tuning
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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 model inputs and parameters, which are not easily optimized to a global optimal point.
Innovation Solution
A computer-implemented method for generating and optimizing artificial intelligence models through automatic data validation, splitting, training, and evaluation, using genetic algorithms and Bayesian optimization to optimize input features and hyperparameters, reducing manual intervention and enabling efficient scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for model generation and optimization, then developers can control the process, but sensitive information is exposed and the process is inefficient
Solution Approach 1:
The system performs automatic model generation, optimization, and scaling without requiring manual developer intervention. The automated system handles data processing, model training, and parameter optimization independently, eliminating the security risks associated with manual access to sensitive data while maintaining full control over the ML pipeline.
2Manufacturing precision
If static inputs and parameters are used in AI models, then model structure is simple, but the model cannot be optimized efficiently to global optimal point
Solution Approach 1:
The system dynamically adjusts model inputs and parameters during the optimization process. Instead of using fixed static values, the system continuously modifies inputs and hyperparameters based on performance feedback, enabling the model to converge to global optimal points while adapting its structure as needed.
Solution Approach 2:
The system systematically changes and optimizes model parameters including input features, hyperparameters, and architecture configurations. By exploring different parameter combinations through automated optimization, the system achieves superior model performance without manual tuning, resolving the contradiction between optimization precision and structural complexity.
3Productivity
If manual optimization is performed on AI models, then developers can adjust parameters, but additional time and resources are required
Solution Approach 1:
The system replaces manual mechanical optimization processes with automated computational algorithms. Instead of developers manually adjusting parameters and evaluating models, the system uses automated optimization algorithms to systematically explore the parameter space, significantly reducing the time and human resources required while accelerating model generation and deployment.
4Adaptability or versatility
If related art manual approaches are used, then model generation is possible, but scaling requires extremely large amounts of computing power and storage
Solution Approach 1:
The system optimizes model parameters and architecture to achieve better efficiency ratios. By automatically tuning parameters for optimal performance-to-resource ratio, the system enables models to scale more effectively with reduced computational requirements, addressing the contradiction between scalability and resource consumption.
Data Source
AI summary
A method for optimizing machine learning model generation, the method comprising obtaining learning data to be used in machine learning model training; generating first generation indices based on a plurality of features of the learning data; generating first machine learning models trained with the learning data and the first generation indices; determining model accuracy for each of the first machine learning models; selecting models of a predetermined number having highest model accuracy from the first machine learning models; generating second generation indices based on second features from generation indices from the first generation indices associated with the models of the predetermined number; generating second machine learning models trained with the learning data and the second features; determining model accuracy for each of the second machine learning models; and selecting a machine learning model having highest model accuracy from the second machine learning models for deployment.