17930835. Automatically Building Efficient Machine Learning Model Training Environments simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 Automatically Building Efficient Machine Learning Model Training Environments
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Automatically Building Efficient Machine Learning Model Training Environments - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Original Abstract Submitted
Automatically Building Efficient Machine Learning Model Training Environments
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Automatically Building Efficient Machine Learning Model Training Environments - A simplified explanation of the abstract
This abstract first appeared for US patent application 17930835 titled 'Automatically Building Efficient Machine Learning Model Training Environments
Simplified Explanation
The abstract describes a method for predicting machine learning model training results and durations based on various input data set properties, model settings, and training environment properties. The goal is to minimize capacity unit hours while achieving successful model training.
- Predict machine learning model training results and durations based on different combinations of input data set properties, model settings, and training environment properties.
- Use a classification model to predict model training results and a regression model to predict training durations.
- Determine capacity unit hours for each combination with predicted successful training results.
- Select the combination with the minimum capacity unit hours for training the machine learning model.
Potential Applications
This technology can be applied in various fields such as healthcare, finance, marketing, and more for optimizing machine learning model training processes.
Problems Solved
1. Efficient prediction of model training results and durations. 2. Minimization of capacity unit hours for successful model training.
Benefits
1. Cost-effective machine learning model training. 2. Improved accuracy in predicting training outcomes. 3. Time-saving in selecting the optimal combination for model training.
Potential Commercial Applications
Optimizing Machine Learning Model Training for Cost-Effective Results
Original Abstract Submitted
Machine learning model training is provided. A model training result of a machine learning model is predicted utilizing a classification model based on a plurality of different combinations of input data set properties, settings of the machine learning model, and machine learning model training environment properties. Model training duration of the machine learning model is predicted utilizing a regression model based on those combinations that had a predicted successful model training result. Capacity unit hours is determined for each respective combination having the predicted successful model training result based on a corresponding predicted model training duration of the machine learning model. A particular combination of input data set properties, settings of the machine learning model, and machine learning model training environment properties that has minimum capacity unit hours is selected. The machine learning model is trained using the particular combination.
