Patents by Inventor Mohamed Fawzi Mokhtar Abd El Aziz

Mohamed Fawzi Mokhtar Abd El Aziz has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Distributed training and prediction using elastic resources
    Patent number: 11003992
    Abstract: In one embodiment, a method includes establishing access to first and second different computing systems. A machine learning model is assigned for training to the first computing system, and the first computing system creates a check-point during training in response to a first predefined triggering event. The check-point may be a record of an execution state in the training of the machine learning model by the first computing system. In response to a second predefined triggering event, the training of the machine learning model on the first computing system is halted, and in response to a third predefined triggering event, the training of the machine learning model is transferred to the second computing system, which continues training the machine learning model starting from the execution state recorded by the check-point.
    Type: Grant
    Filed: October 16, 2017
    Date of Patent: May 11, 2021
    Assignee: Facebook, Inc.
    Inventors: Lukasz Wesolowski, Mohamed Fawzi Mokhtar Abd El Aziz, Aditya Rajkumar Kalro, Hongzhong Jia, Jay Parikh
  • DISTRIBUTED TRAINING AND PREDICTION USING ELASTIC RESOURCES
    Publication number: 20190114537
    Abstract: In one embodiment, a method includes establishing access to first and second different computing systems. A machine learning model is assigned for training to the first computing system, and the first computing system creates a check-point during training in response to a first predefined triggering event. The check-point may be a record of an execution state in the training of the machine learning model by the first computing system. In response to a second predefined triggering event, the training of the machine learning model on the first computing system is halted, and in response to a third predefined triggering event, the training of the machine learning model is transferred to the second computing system, which continues training the machine learning model starting from the execution state recorded by the check-point.
    Type: Application
    Filed: October 16, 2017
    Publication date: April 18, 2019
    Inventors: Lukasz Wesolowski, Mohamed Fawzi Mokhtar Abd El Aziz, Aditya Rajkumar Kalro, Hongzhong Jia, Jay Parikh