18461378. Management Of Multiple Machine Learning Model Pipelines simplified abstract (Oracle International Corporation)
Contents
- 1 Management Of Multiple Machine Learning Model Pipelines
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Management Of Multiple Machine Learning Model Pipelines - 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
- 1.10 How does the software service handle data privacy and security concerns?
- 1.11 What is the scalability of the software service in terms of handling a large number of users and ML applications?
- 1.12 Original Abstract Submitted
Management Of Multiple Machine Learning Model Pipelines
Organization Name
Oracle International Corporation
Inventor(s)
Andrew Ioannou of San Francisco CA (US)
[[:Category:Miroslav Nov�k of Prague (CZ)|Miroslav Nov�k of Prague (CZ)]][[Category:Miroslav Nov�k of Prague (CZ)]]
Hari Ganesh Natarajan of Redmond WA (US)
David Kalivoda of Pardubice (CZ)
Management Of Multiple Machine Learning Model Pipelines - A simplified explanation of the abstract
This abstract first appeared for US patent application 18461378 titled 'Management Of Multiple Machine Learning Model Pipelines
Simplified Explanation
The software service described in the abstract allows software providers to easily implement machine learning features into their products. Here are some key points to explain this innovation:
- Central repository for encapsulated ML applications
- Provisioning ML features for each user on an as-needed basis
- Central definition for ML applications that encapsulate data science and processing capabilities
- Replication of ML deployment pipeline template for user-specific ML models and predictions
Potential Applications
The technology can be applied in various industries such as healthcare, finance, e-commerce, and more to enhance decision-making processes, improve customer experiences, and optimize operations.
Problems Solved
1. Simplifies the implementation of machine learning features for software providers 2. Enables customization of ML models for each user based on central ML applications
Benefits
1. Streamlines the deployment of ML features 2. Increases efficiency in developing user-specific ML models 3. Enhances the overall user experience by providing tailored predictions
Potential Commercial Applications
Optimizing marketing campaigns, personalizing recommendations, fraud detection, predictive maintenance, and more in various industries.
Possible Prior Art
One possible prior art could be the use of centralized ML models in cloud computing environments to provide scalable and customizable machine learning solutions for users.
Unanswered Questions
How does the software service handle data privacy and security concerns?
The article does not delve into the specifics of how data privacy and security are maintained within the software service.
What is the scalability of the software service in terms of handling a large number of users and ML applications?
The scalability aspect of the software service is not addressed in the article.
Original Abstract Submitted
In one or more embodiments, a software service allows software providers to implement machine learning (ML) features into products offered by the software providers. Each ML feature may be referred to as an encapsulated ML application, which may be defined and maintained in a central repository, while also being provisioned for each user of the software provider on an as-needed basis. Advantageously, embodiments allow for a central definition for an ML application that encapsulates data science and processing capabilities and routines of the software provider. This central ML application delivers a ML deployment pipeline template that may be replicated multiple times as separate, tailored runtime pipeline instances on a per-user basis. Each runtime pipeline instance accounts for differences in the specific data of each user, resulting in user-specific ML models and predictions based on the same central ML application.
