View source for Oracle international corporation (20240127119). Management Of Multiple Machine Learning Model Pipelines simplified abstract

=Management Of Multiple Machine Learning Model Pipelines=


==Organization Name==

[[:Category:oracle international corporation|oracle international corporation]]


[[Category:oracle international corporation]]

==Inventor(s)==

[[:Category:Andrew Ioannou of San Francisco CA (US)|Andrew Ioannou of San Francisco CA (US)]][[Category: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)]]

[[:Category:Petr Dousa of Prague (CZ)|Petr Dousa of Prague (CZ)]][[Category:Petr Dousa of Prague (CZ)]]

[[:Category:Martin Panacek of Zlin (CZ)|Martin Panacek of Zlin (CZ)]][[Category:Martin Panacek of Zlin (CZ)]]

[[:Category:Hari Ganesh Natarajan of Redmond WA (US)|Hari Ganesh Natarajan of Redmond WA (US)]][[Category:Hari Ganesh Natarajan of Redmond WA (US)]]

[[:Category:David Kalivoda of Pardubice (CZ)|David Kalivoda of Pardubice (CZ)]][[Category:David Kalivoda of Pardubice (CZ)]]

[[:Category:Vojtech Janota of Prague (CZ)|Vojtech Janota of Prague (CZ)]][[Category:Vojtech Janota of Prague (CZ)]]

[[:Category:Zdenek Pesek of Teplice (CZ)|Zdenek Pesek of Teplice (CZ)]][[Category:Zdenek Pesek of Teplice (CZ)]]

[[:Category:Jan Pridal of Olomouc (CZ)|Jan Pridal of Olomouc (CZ)]][[Category:Jan Pridal of Olomouc (CZ)]]

==Management Of Multiple Machine Learning Model Pipelines - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240127119 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. Each machine learning feature is defined and maintained in a central repository and provisioned for each user on an as-needed basis. This central definition streamlines the process of creating user-specific machine learning models and predictions based on the same core machine learning application.

* Central repository for machine learning features
* Provisioning machine learning features for each user on demand
* Streamlined process for creating user-specific machine learning models and predictions

== Potential Applications ==
The technology described in this patent application could be applied in various industries such as healthcare, finance, e-commerce, and marketing where personalized machine learning models are needed to provide tailored solutions for users.

== Problems Solved ==
This technology solves the problem of efficiently implementing machine learning features into software products and creating user-specific machine learning models without the need for extensive customization for each user.

== Benefits ==
The benefits of this technology include faster deployment of machine learning features, improved scalability, and the ability to provide personalized solutions to users based on a central machine learning application.

== Potential Commercial Applications ==
The technology could be commercially applied in software development companies, SaaS providers, and businesses looking to enhance their products with machine learning capabilities to offer personalized services to their customers.

== Possible Prior Art ==
One possible prior art for this technology could be the use of machine learning templates in software development to streamline the process of creating user-specific machine learning models. Additionally, the concept of central repositories for machine learning applications may have been explored in the past.

=== Unanswered Questions ===

=== How does this technology handle data privacy and security concerns? ===
The article does not address how user-specific data is handled and secured within the system to ensure privacy and compliance with regulations.

=== What is the scalability of this technology in terms of handling a large number of users and machine learning models? ===
The article does not provide information on the scalability of the system and how it can efficiently manage a growing user base and the associated machine learning models.


==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.



[[Category:G06N20/00]]