18584099. ACCELERATED MACHINE LEARNING simplified abstract (International Business Machines Corporation)
Contents
ACCELERATED MACHINE LEARNING
Organization Name
International Business Machines Corporation
Inventor(s)
Tejaswini Pedapati of White Plains NY (US)
ACCELERATED MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18584099 titled 'ACCELERATED MACHINE LEARNING
Simplified Explanation: This patent application describes a method for accelerating machine learning in a computing environment by scoring machine learning pipelines based on learning curves and terminating training on pipelines that do not meet a certain threshold.
- Machine learning pipelines are trained on selected data.
- Each pipeline is scored based on learning curves during training.
- Training is completed for pipelines with scores above a threshold.
- Training is terminated for pipelines with scores below the threshold.
Key Features and Innovation:
- Scoring machine learning pipelines based on learning curves.
- Accelerating machine learning by focusing resources on high-scoring pipelines.
- Efficiently training machine learning models by terminating training on low-scoring pipelines.
Potential Applications: This technology can be applied in various industries such as healthcare, finance, e-commerce, and more for accelerating machine learning processes and improving model training efficiency.
Problems Solved:
- Optimizing machine learning training processes.
- Efficiently allocating computing resources.
- Improving the overall performance of machine learning models.
Benefits:
- Faster machine learning model training.
- Resource optimization.
- Enhanced model performance.
Commercial Applications: Accelerating machine learning processes in industries such as healthcare, finance, and e-commerce can lead to improved decision-making, personalized recommendations, and enhanced customer experiences.
Prior Art: Readers can explore prior research on machine learning pipeline optimization, learning curve analysis, and resource allocation in machine learning training.
Frequently Updated Research: Stay informed about the latest advancements in machine learning pipeline optimization, learning curve analysis, and resource allocation strategies for efficient model training.
Questions about Machine Learning Acceleration: 1. How does scoring machine learning pipelines based on learning curves improve training efficiency? 2. What are the potential implications of terminating training on low-scoring pipelines in machine learning acceleration?
Original Abstract Submitted
Various embodiments are provided for accelerating machine learning in a computing environment by one or more processors in a computing system. Selected data may be received for training machine learning pipelines. Each of the machine learning pipelines may be scored according to one or more learning curves while training on selected data. Completion of the training on the selected data may be permitted for those of the machine learning pipelines having a score greater than a selected threshold. The training on the selected data may be terminated, prior to completion, on those of the machine learning pipelines having a score less than a selected threshold.
