17937319. CONTINUAL MACHINE LEARNING IN A PROVIDER NETWORK simplified abstract (Amazon Technologies, Inc.)
Contents
- 1 CONTINUAL MACHINE LEARNING IN A PROVIDER NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTINUAL MACHINE LEARNING IN A PROVIDER NETWORK - 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 Possible Prior Art
- 1.9.1 Unanswered Questions
- 1.9.2 How does this technology handle privacy and security concerns related to continual learning in a provider network?
- 1.9.3 What are the potential limitations or challenges faced when implementing user-configurable model retraining or hyperparameter tuning in a continual learning system?
- 1.10 Original Abstract Submitted
CONTINUAL MACHINE LEARNING IN A PROVIDER NETWORK
Organization Name
Inventor(s)
Giovanni Zappella of Berlin (DE)
Lukas Stefan Balles of Berlin (DE)
Martin Wistuba of Salzkotten (DE)
Cedric Philippe Archambeau of Berlin (DE)
CONTINUAL MACHINE LEARNING IN A PROVIDER NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 17937319 titled 'CONTINUAL MACHINE LEARNING IN A PROVIDER NETWORK
Simplified Explanation
The abstract describes a system and method for continual learning in a provider network, utilizing a semi-automated or fully automated architecture of continual machine learning with user-configurable model retraining or hyperparameter tuning.
- The system enables continual learning in a provider network.
- It implements a semi-automated or fully automated architecture of continual machine learning.
- The architecture allows for user-configurable model retraining or hyperparameter tuning.
- The system adapts a model over time to new information in the training data.
- It provides a user-friendly, flexible, and customizable continual learning process.
Potential Applications
This technology could be applied in various industries such as healthcare, finance, cybersecurity, and marketing where continual learning and adaptation to new data are crucial.
Problems Solved
1. Adapting machine learning models to new information over time. 2. Providing a user-friendly and customizable continual learning process.
Benefits
1. Improved accuracy and performance of machine learning models. 2. Increased efficiency in adapting to new data. 3. Customizable and flexible learning process.
Potential Commercial Applications
Optimizing marketing campaigns, enhancing cybersecurity measures, improving healthcare diagnostics, and streamlining financial forecasting processes could all benefit from this technology.
Possible Prior Art
One potential prior art could be the use of transfer learning techniques in machine learning to adapt models to new data over time.
Unanswered Questions
The article does not address the specific mechanisms or protocols in place to ensure data privacy and security while implementing continual learning in a provider network.
What are the potential limitations or challenges faced when implementing user-configurable model retraining or hyperparameter tuning in a continual learning system?
The article does not delve into the potential obstacles or difficulties that may arise when allowing users to configure model retraining or hyperparameter tuning in a continual learning system.
Original Abstract Submitted
A system and method for continual learning in a provider network. The method is configured to implement or interface with a system which implements a semi-automated or fully automated architecture of continual machine learning, the semi-automated or fully automated architecture implementing user-configurable model retraining or hyperparameter tuning, which is enabled by a provider network. This functions to adapt a model over time to new information in the training data while also providing a user-friendly, flexible, and customizable continual learning process.
