View source for 18152528. TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS simplified abstract (NVIDIA Corporation)

=TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS=


==Organization Name==

[[:Category:NVIDIA Corporation|NVIDIA Corporation]]


[[Category:NVIDIA Corporation]]

==Inventor(s)==

[[:Category:Jason Lavar Clemons of Leander TX (US)|Jason Lavar Clemons of Leander TX (US)]][[Category:Jason Lavar Clemons of Leander TX (US)]]

[[:Category:Kavya Sreedhar of Oakland CA (US)|Kavya Sreedhar of Oakland CA (US)]][[Category:Kavya Sreedhar of Oakland CA (US)]]

==TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18152528 titled 'TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS

The abstract of this patent application describes techniques for allocating computational resources when executing trained machine learning models. This involves determining available computational resources, allocating resources based on performance requirements, and causing the models to perform tasks using the allocated resources.

* The techniques involve determining available computational resources for trained machine learning models.
* Computational resources are allocated based on performance requirements associated with tasks.
* Trained machine learning models are then directed to perform tasks using the allocated computational resources.

Potential Applications:
- This technology can be applied in various industries such as healthcare, finance, and autonomous vehicles.
- It can be used for optimizing resource allocation in cloud computing environments.

Problems Solved:
- Efficient allocation of computational resources for trained machine learning models.
- Meeting performance requirements for tasks executed by machine learning models.

Benefits:
- Improved performance and efficiency of machine learning model execution.
- Optimal utilization of computational resources leading to cost savings.

Commercial Applications:
Title: "Optimized Computational Resource Allocation for Machine Learning Models"
This technology can be utilized by cloud service providers, data centers, and companies utilizing machine learning for various applications.

Questions about Optimized Computational Resource Allocation for Machine Learning Models:
1. How does this technology improve the efficiency of machine learning model execution?
2. What are the potential cost savings associated with optimal resource allocation for machine learning tasks?

Frequently Updated Research:
Stay updated on advancements in resource allocation techniques for machine learning models to ensure optimal performance and efficiency.


==Original Abstract Submitted==

Techniques are disclosed herein for allocating computational resources when executing trained machine learning models. The techniques include determining one or more available computational resources that are usable by one or more trained machine learning models to perform one or more tasks, allocating one or more computational resources to the one or more tasks based on the one or more available computational resources and one or more performance requirements associated with the one or more tasks, and causing the one or more trained machine learning models to perform the one or more tasks using the one or more computational resources allocated to the one or more tasks.



[[Category:G06F9/50]]
[[Category:G06N20/00]]
[[Category:CPC_G06F9/5038]]