View source for Telefonaktiebolaget lm ericsson (publ) (20240135247). Method and Apparatus for Selecting Machine Learning Model for Execution in a Resource Constraint Environment simplified abstract

=Method and Apparatus for Selecting Machine Learning Model for Execution in a Resource Constraint Environment=


==Organization Name==

[[:Category:telefonaktiebolaget lm ericsson (publ)|telefonaktiebolaget lm ericsson (publ)]]


[[Category:telefonaktiebolaget lm ericsson (publ)]]

==Inventor(s)==

[[:Category:Andreas Johnsson of Uppsala (SE)|Andreas Johnsson of Uppsala (SE)]][[Category:Andreas Johnsson of Uppsala (SE)]]

[[:Category:Rerngvit Yanggratoke of Järfälla (SE)|Rerngvit Yanggratoke of Järfälla (SE)]][[Category:Rerngvit Yanggratoke of Järfälla (SE)]]

==Method and Apparatus for Selecting Machine Learning Model for Execution in a Resource Constraint Environment - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240135247 titled 'Method and Apparatus for Selecting Machine Learning Model for Execution in a Resource Constraint Environment

==Simplified Explanation==

The abstract describes a method for selecting a machine learning model to be deployed in an execution environment with resource constraints. The method involves receiving a request for a machine learning model, retrieving a set of models that solve the task using a subset of features, calculating the complexity of each model, and determining a suitable model based on complexity and resource constraints.

* The method involves receiving a request for a machine learning model to solve a specific task using a set of features.
* It retrieves a set of machine learning models from a model store that can solve the task using at least a subset of the features.
* The complexity of each model in the set is calculated to determine the most suitable model based on resource constraints.
* The selected model is then deployed in the execution environment.

== Potential Applications ==
The technology can be applied in various fields such as healthcare, finance, marketing, and more where deploying machine learning models with resource constraints is crucial.

== Problems Solved ==
This technology solves the problem of efficiently selecting and deploying machine learning models in environments with limited resources, ensuring optimal performance and utilization.

== Benefits ==
The method allows for the efficient deployment of machine learning models in resource-constrained environments, maximizing performance while minimizing resource usage.

== Potential Commercial Applications ==
Potential commercial applications include deploying machine learning models in edge computing devices, IoT devices, and other resource-constrained environments to enhance decision-making processes.

== Possible Prior Art ==
One possible prior art could be the use of model selection algorithms in machine learning to optimize model performance based on resource constraints.

=== What are the specific resource constraints considered in the method described in the abstract? ===

The specific resource constraints considered in the method described in the abstract include limitations on computational power, memory, and storage capacity of the execution environment.

=== How does the method calculate the complexity of each machine learning model in the set? ===

The method calculates the complexity of each machine learning model in the set based on factors such as the number of features used, the computational requirements, and the memory footprint of the model.


==Original Abstract Submitted==

embodiments herein disclose a method for selecting a machine learning model to be deployed in an execution environment having resource constraints. the method comprises receiving, by an apparatus, a request for a machine learning model solving a task t using a feature set f. further, the method includes retrieving, from a model store, a first set of machine learning models that solves the task t using at least a subset of features f. the complexity of each machine learning model in the first set of machine learning models is calculated. the method includes determining, from the first set of machine learning models, at least one suitable machine learning model to be deployed, wherein the determining is based on the calculated complexity and the resource constraints of the execution environment.



[[Category:G06N20/00]]