View source for 18610561. MULTIPLE MODEL INJECTION FOR A DEPLOYMENT CLUSTER simplified abstract (Hewlett Packard Enterprise Development LP)

=MULTIPLE MODEL INJECTION FOR A DEPLOYMENT CLUSTER=


==Organization Name==

[[:Category:Hewlett Packard Enterprise Development LP|Hewlett Packard Enterprise Development LP]]


[[Category:Hewlett Packard Enterprise Development LP]]

==Inventor(s)==

[[:Category:Kartik Mathur of Santa Clara CA (US)|Kartik Mathur of Santa Clara CA (US)]][[Category:Kartik Mathur of Santa Clara CA (US)]]

==MULTIPLE MODEL INJECTION FOR A DEPLOYMENT CLUSTER - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18610561 titled 'MULTIPLE MODEL INJECTION FOR A DEPLOYMENT CLUSTER

'''Simplified Explanation:'''
The patent application describes a system for servicing inference requests by multiple machine learning models in a deployment cluster without tight coupling between the API server and the models.

'''Key Features and Innovation:'''
* System for servicing inference requests by multiple machine learning models in a deployment cluster
* API server not tightly coupled to any specific machine learning model
* Retrieval of configuration parameters for target model identified in the inference request
* Utilization of retrieved parameters to service the inference request and return results to a business system application

'''Potential Applications:'''
This technology can be applied in various industries such as healthcare, finance, e-commerce, and more for efficient processing of inference requests by machine learning models.

'''Problems Solved:'''
The technology addresses the challenge of efficiently servicing inference requests by multiple machine learning models in a deployment cluster without the need for tight coupling between the API server and the models.

'''Benefits:'''
* Improved scalability and flexibility in servicing inference requests
* Enhanced performance and efficiency in processing machine learning tasks
* Seamless integration with business system applications for streamlined operations

'''Commercial Applications:'''
The technology can be utilized in cloud computing services, data analytics platforms, and AI-driven applications to optimize machine learning model deployment and inference processing for various commercial purposes.

'''Prior Art:'''
Readers can explore prior research on machine learning model deployment and inference processing in deployment clusters to understand the evolution of similar technologies in the field.

'''Frequently Updated Research:'''
Stay updated on advancements in machine learning model deployment, inference processing, and cluster management to leverage the latest innovations in the field.

'''Questions about Machine Learning Model Deployment and Inference Processing:'''
1. What are the key challenges in efficiently servicing inference requests by multiple machine learning models in a deployment cluster?
2. How does the technology described in the patent application improve the scalability and flexibility of machine learning model deployment and inference processing?


==Original Abstract Submitted==

Systems and methods are provided for servicing inference request by one of multiple machine learning models attached to a deployment cluster. The API server of a deployment cluster is not tightly coupled to any of multiple machine learning models attached to the deployment cluster. Upon receiving an inference request, the deployment cluster can retrieve the configuration parameters, including serialization formatting, for a target model identified in the inference request. The deployment cluster can utilize the retrieved parameters to service the inference request and return the results to a business system application.



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