公开(公告)号：US20220172004A1

公开(公告)日：2022-06-02

申请号：US17106027

申请日：2020-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Sanjiv Das ,  Michele Donini ,  Jason Lawrence Gelman ,  Kevin Haas ,  Tyler Stephen Hill ,  Krishnaram Kenthapadi ,  Pinar Altin Yilmaz ,  Muhammad Bilal Zafar ,  Pedro L. Larroy

IPC: G06K9/62 ,  G06N20/00 ,  G06F11/34 ,  G06F9/54

Abstract: Bias metrics and feature attribution may be monitored for a machine learning model. A request to enable monitoring for bias metrics or feature attribution may be received. Monitoring may be enabled to evaluate respective performance of inferences of a machine learning model according to the enabled bias metrics or feature attribution. If a divergence from reference data is detected, then a notification indicating the divergence may be sent.

公开(公告)号：US20220172099A1

公开(公告)日：2022-06-02

申请号：US17106013

申请日：2020-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Sanjiv Das ,  Michele Donini ,  Jason Lawrence Gelman ,  Kevin Haas ,  Tyler Stephen Hill ,  Krishnaram Kenthapadi ,  Pinar Altin Yilmaz ,  Muhammad Bilal Zafar ,  Pedro L Larroy

IPC: G06N20/00 ,  G06K9/62

Abstract: Bias metrics may be captured at different stages for training a machine learning model. A training job may specify bias metrics to capture at multiple different stages of a machine learning pipeline for a feature of a training data set used to train a machine learning model. The training job may be executed and the bias metrics determined at the stages as specified in the training job. The bias metrics for the different stages may be stored.

公开(公告)号：US20220171991A1

公开(公告)日：2022-06-02

申请号：US17106021

申请日：2020-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Sanjiv Das ,  Michele Donini ,  Jason Lawrence Gelman ,  Kevin Haas ,  Tyler Stephen Hill ,  Krishnaram Kenthapadi ,  Pinar Altin Yilmaz ,  Muhammad Bilal Zafar ,  Pedro L Larroy

IPC: G06K9/62 ,  G06N20/00 ,  H04L29/08

Abstract: Views may be generated for bias metrics or feature attribution captured in machine learning pipelines. A request to create a view of bias metrics or feature attribution may be received. The bias metrics or feature attribution may have been determined in a machine learning pipeline as part of executing a training job that specified the bias metrics or the feature attribution. A development application may access a data store that stores the bias metrics or the feature attribution determined in the machine learning pipeline. A view based on the bias metrics or feature attribution may be generated and provided.

公开(公告)号：US20220172101A1

公开(公告)日：2022-06-02

申请号：US17106029

申请日：2020-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Sanjiv Das ,  Michele Donini ,  Jason Lawrence Gelman ,  Kevin Haas ,  Tyler Stephen Hill ,  Krishnaram Kenthapadi ,  Pinar Altin Yilmaz ,  Muhammad Bilal Zafar ,  Pedro L Larroy

IPC: G06N20/00 ,  G06N5/04

Abstract: Feature attribution may be captured as part of a machine learning pipeline. A training job may include a request to determine feature attribution as part of a machine learning pipeline that trains a machine learning model from a training data set. A reference data set for determining the feature attribution of the machine learning model may be identified. The feature attribution may be determined based on the reference data set. The feature attribution of the trained machine learning model may be stored.