公开(公告)号：US20230168941A1

公开(公告)日：2023-06-01

申请号：US17538663

申请日：2021-11-30

Applicant: Adobe Inc.

Inventor： Subrata Mitra ,  Sunav Choudhary ,  Shaddy Garg ,  Anuj Jitendra Diwan ,  Piyush Kumar Maurya ,  Arpit Aggarwal ,  Prateek Jain

IPC: G06F9/50 ,  G06K9/62 ,  G06N20/00

CPC classification number: G06F9/5038 ,  G06F9/5044 ,  G06F9/5055 ,  G06F9/5088 ,  G06K9/6256 ,  G06N20/00

Abstract: A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.

公开(公告)号：US11593634B2

公开(公告)日：2023-02-28

申请号：US16012356

申请日：2018-06-19

Applicant: Adobe Inc.

Inventor： Sunav Choudhary ,  Saurabh Kumar Mishra ,  Manoj Ghuhan A ,  Ankur Garg

IPC: G06N3/08 ,  G06N3/04 ,  G06N7/00 ,  G06N20/00 ,  G06F16/25 ,  G06F13/42

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that asynchronously train a machine learning model across client devices that implement local versions of the model while preserving client data privacy. To train the model across devices, in some embodiments, the disclosed systems send global parameters for a global machine learning model from a server device to client devices. A subset of the client devices uses local machine learning models corresponding to the global model and client training data to modify the global parameters. Based on those modifications, the subset of client devices sends modified parameter indicators to the server device for the server device to use in adjusting the global parameters. By utilizing the modified parameter indicators (and not client training data), in certain implementations, the disclosed systems accurately train a machine learning model without exposing training data from the client device.

公开(公告)号：US20200218721A1

公开(公告)日：2020-07-09

申请号：US16821132

申请日：2020-03-17

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Sunav Choudhary ,  Gaurush Hiranandani

IPC: G06F16/2458 ,  G06F16/248

Abstract: Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.