International business machines corporation (20240193486). ACCELERATED MACHINE LEARNING simplified abstract
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 20240193486 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.
- **Key Features and Innovation:**
- Accelerating machine learning processes in a computing system. - Scoring machine learning pipelines based on learning curves. - Terminating training on pipelines that do not meet a specified threshold.
- **Potential Applications:**
- This technology can be applied in various industries such as healthcare, finance, and e-commerce for optimizing machine learning processes.
- **Problems Solved:**
- Efficiently accelerating machine learning training processes. - Automatically terminating training on underperforming machine learning pipelines.
- **Benefits:**
- Improved efficiency in machine learning training. - Reduction in computational resources and time required for training.
- **Commercial Applications:**
- "Optimizing Machine Learning Training Processes for Enhanced Performance in Various Industries"
- **Prior Art:**
- Researchers have explored methods to optimize machine learning training, but this specific approach of scoring pipelines based on learning curves may be novel.
- **Frequently Updated Research:**
- Stay updated on advancements in machine learning optimization techniques and algorithms to enhance the efficiency of training processes.
Questions about Machine Learning Acceleration: 1. What are some common challenges faced in accelerating machine learning processes? 2. How does scoring machine learning pipelines based on learning curves improve efficiency in training?
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.
