ML Training Settings Prediction for Target Metric Values
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of tuning machine learning (ML) engine settings for specific performance targets is tedious, resource-intensive, and challenging due to the high number of settings, often requiring multiple iterations, which can be inefficient and computationally demanding.
Innovation Solution
A method that utilizes previous sets of settings and their associated performance metrics to determine optimal settings for generating machine learning models, using a predictive model to estimate the required settings based on desired performance targets, thereby reducing the need for manual tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning of ML engine settings is performed to achieve desired performance targets, then model performance can be optimized, but the process becomes tedious and resource-intensive
Solution Approach 1:
The system performs preliminary actions by automatically determining optimal training settings before model training begins. It uses obtained settings from previous operations to predict and set appropriate parameters in advance, eliminating the need for manual tuning iterations and saving time while achieving desired performance targets.
Solution Approach 2:
The ML engine performs self-service by automatically adjusting its own training settings based on obtained settings from previous operations. The system determines appropriate settings autonomously without requiring external manual intervention, thereby reducing tuning time while maintaining performance optimization.
2Measurement precision
If multiple iterations of setting adjustments are performed to achieve performance targets, then model accuracy improves, but computational resources are consumed
Solution Approach 1:
The system performs preliminary determination of optimal settings using obtained settings from previous operations before actual model training. By predicting appropriate training parameters in advance based on historical data, it avoids multiple iterative adjustments during training, thereby reducing computational resource consumption while achieving target accuracy.
Solution Approach 2:
The system uses feedback from obtained settings of previous operations to inform and optimize current training settings. By leveraging historical performance data and settings, it creates a feedback loop that enables accurate prediction of optimal parameters, reducing the need for resource-intensive iterative tuning while maintaining model accuracy.
3Measurement precision
If manual tuning of multiple settings is performed, then desired performance targets can be achieved, but the complexity of the process increases
Solution Approach 1:
The ML engine performs self-service by automatically determining its own training settings based on obtained settings from previous operations. This automation eliminates the need for manual configuration of multiple parameters, simplifying the operation process while still achieving desired performance targets through intelligent setting selection.
Solution Approach 2:
The system introduces an intermediary mechanism that automatically translates obtained settings from previous operations into appropriate current training settings. This intermediary process handles the complexity of multiple parameter adjustments, presenting a simplified interface to users while maintaining precise control over model performance.
4Measurement precision
If extensive setting adjustments are made to optimize model performance, then prediction accuracy improves, but the time required for model deployment increases
Solution Approach 1:
The system performs preliminary determination of optimal training settings using obtained settings from previous operations before model training and deployment. By predicting appropriate settings in advance based on historical data, it achieves the desired prediction accuracy without requiring time-consuming iterative adjustments, thereby accelerating the overall deployment timeline.
Data Source
AI summary
The present disclosure relates to a method for generating a machine learning (ML) model. The method comprises: receiving a selection of a desired value of the metric for predicting a value of the first label attribute based on a current training dataset. Previously obtained sets of training settings may be used for determining a set of training settings that corresponds to the desired value of the metric. A ML engine may be controlled to generate using the current training dataset a machine learning model according to the determined set of training settings. The generated machine learning model may be deployed for performing predictions of values of the first label attribute.