公开(公告)号：US20160300156A1

公开(公告)日：2016-10-13

申请号：US14684041

申请日：2015-04-10

Applicant: Facebook, Inc.

Inventor： Stuart Michael Bowers ,  Parul Agarwal ,  Parv Ajay Oberoi ,  Hussein Mohamed Hassan Mehanna

IPC: G06N99/00

CPC classification number: G06N99/005 ,  G06F8/00 ,  G06F9/46 ,  G06F17/30539

Abstract: Some embodiments include a machine learner platform. The machine learner platform can implement a model tracking service to track one or more machine learning models for one or more application services. A model tracker database can record a version history and/or training configurations of the machine learning models. The machine learner platform can implement a platform interface configured to present interactive controls for building, modifying, evaluating, deploying, or compare the machine learning models. A model trainer engine can task out a model training task to one or more computing devices. A model evaluation engine can compute an evaluative metric for a resulting model from the model training task.

Abstract translation: 一些实施例包括机器学习者平台。 机器学习者平台可以实现模型跟踪服务，以跟踪一个或多个应用服务的机器学习模型。 模型跟踪器数据库可以记录机器学习模型的版本历史和/或训练配置。 机器学习平台可以实现一个平台接口，配置为提供用于构建，修改，评估，部署或比较机器学习模型的交互式控件。 模型训练器引擎可以将模型训练任务排除到一个或多个计算设备。 模型评估引擎可以从模型训练任务计算结果模型的评估度量。

公开(公告)号：US09996804B2

公开(公告)日：2018-06-12

申请号：US14684041

申请日：2015-04-10

Applicant: Facebook, Inc.

Inventor： Stuart Michael Bowers ,  Parul Agarwal ,  Parv Ajay Oberoi ,  Hussein Mohamed Hassan Mehanna

IPC: G06N99/00 ,  G06F9/44 ,  G06F9/46 ,  G06F17/30

CPC classification number: G06N99/005 ,  G06F8/00 ,  G06F9/46 ,  G06F17/30539

Abstract: Some embodiments include a machine learner platform. The machine learner platform can implement a model tracking service to track one or more machine learning models for one or more application services. A model tracker database can record a version history and/or training configurations of the machine learning models. The machine learner platform can implement a platform interface configured to present interactive controls for building, modifying, evaluating, deploying, or compare the machine learning models. A model trainer engine can task out a model training task to one or more computing devices. A model evaluation engine can compute an evaluative metric for a resulting model from the model training task.