Dell products l.p. (20240177028). SYSTEM AND METHOD FOR EXECUTING MULTIPLE INFERENCE MODELS USING INFERENCE MODEL PRIORITIZATION simplified abstract
Contents
- 1 SYSTEM AND METHOD FOR EXECUTING MULTIPLE INFERENCE MODELS USING INFERENCE MODEL PRIORITIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR EXECUTING MULTIPLE INFERENCE MODELS USING INFERENCE MODEL PRIORITIZATION - 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 Original Abstract Submitted
SYSTEM AND METHOD FOR EXECUTING MULTIPLE INFERENCE MODELS USING INFERENCE MODEL PRIORITIZATION
Organization Name
Inventor(s)
OFIR Ezrielev of Beer Sheva (IL)
JEHUDA Shemer of Kfar Saba (IL)
SYSTEM AND METHOD FOR EXECUTING MULTIPLE INFERENCE MODELS USING INFERENCE MODEL PRIORITIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177028 titled 'SYSTEM AND METHOD FOR EXECUTING MULTIPLE INFERENCE MODELS USING INFERENCE MODEL PRIORITIZATION
Simplified Explanation
The patent application describes methods and systems for managing the execution of inference models across multiple data processing systems. The system includes an inference model manager and multiple data processing systems.
- The inference model manager obtains operational capability data for the inference models from the data processing systems.
- The manager uses the operational capability data to determine if the data processing systems have enough computing resources to execute the inference models in a timely manner.
- If the systems lack sufficient resources, the manager re-assigns systems to balance the load and ensure continued operation of the models.
Potential Applications
This technology could be applied in various industries such as healthcare, finance, and manufacturing for optimizing data processing and inference model execution.
Problems Solved
This technology solves the problem of inefficient resource allocation in executing inference models across multiple data processing systems, leading to improved performance and reliability.
Benefits
The benefits of this technology include optimized resource utilization, improved inference model execution efficiency, and enhanced overall system performance.
Potential Commercial Applications
One potential commercial application of this technology could be in cloud computing services for optimizing resource allocation and improving the performance of machine learning models.
Possible Prior Art
Prior art in this field may include research papers or patents related to resource management in distributed computing systems or machine learning model execution across multiple systems.
Unanswered Questions
How does this technology impact the scalability of data processing systems?
This article does not delve into how this technology affects the scalability of data processing systems. It would be interesting to explore how the re-assignment of systems impacts the overall scalability of the infrastructure.
What are the potential security implications of re-assigning data processing systems in real-time?
The article does not address the security aspects of re-assigning systems to balance computing resources. It would be crucial to understand the security implications of such actions to ensure data integrity and system protection.
Original Abstract Submitted
methods and systems for managing execution of inference models across multiple data processing systems are disclosed. to manage execution of inference models across multiple data processing systems, a system may include an inference model manager and any number of data processing systems. the inference model manager may obtain operational capability data for the inference models from the data processing systems. the inference model manager may use the operational capability data to determine whether the data processing systems have access to sufficient computing resources to complete timely execution of the inference models. if the data processing systems do not have access to sufficient computing resources to complete timely execution of the inference models, the inference model manager may re-assign one or more data processing systems to re-balance the computing resource load and support continued operation of at least a portion of the inference models.
