METHOD AND SYSTEM FOR OPTIMIZING MACHINE LEARNING ALGORITHMS

Abstract

The present invention provides a method and system for dynamically optimizing machine learning algorithms by continuously monitoring real-time performance metrics, including accuracy, convergence time, and resource utilization. The system adjusts model hyperparameters, architecture, and optimization techniques based on feedback from these metrics to enhance model performance, minimize training time, and reduce computational resource consumption. The invention employs adaptive optimization techniques, such as adjusting the learning rate or switching between optimization algorithms, and integrates resource-aware adjustments to ensure efficient use of computational resources in real-time. The system also includes a predictive modeling component that forecasts performance and resource usage before training begins, allowing proactive adjustments to model configurations. This dynamic optimization framework is applicable across various machine learning models, including supervised, unsupervised, and reinforcement le

Specification

Description:The embodiments of the present invention generally relates generally to machine learning (ML) and optimization techniques, and more specifically, to methods and systems for dynamically optimizing machine learning algorithms. The invention aims to enhance the performance of ML models by continuously adjusting various parameters such as hyperparameters, model architecture, and optimization strategies based on real-time performance metrics, resource constraints, and predictive modeling. This invention is particularly useful in applications where ML models need to balance accuracy, training time, and computational efficiency.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with resp , Claims:1. A method for optimizing machine learning algorithms, comprising:
continuously monitoring performance metrics of a machine learning model during training and testing phases, including at least one of the group consisting of model accuracy, convergence time, and resource utilization;
dynamically adjusting at least one of the model's hyperparameters, architecture, or optimization techniques based on real-time feedback from the performance metrics;
updating the model in real-time to improve performance, wherein the adjustments minimize training time, enhance accuracy, and reduce resource consumption.

2. The method of claim 1, wherein the hyperparameters include at least one of learning rate, batch size, or regularization parameters.

3. The method of claim 1, wherein the model architecture is adjusted by modifying the number of layers, layer types, or activation functions in the neural network.

4. The method of claim 1, wherein the optimization techniques include stochastic gradient descent or Adam optimizat

Documents