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    MACHINE LEARNING SERVICE
    1.
    发明申请
    MACHINE LEARNING SERVICE 有权

    公开(公告)号：US20250021884A1

    公开(公告)日：2025-01-16

    申请号：US18775912

    申请日：2024-07-17

    Applicant: Amazon Technologies, Inc.

    Inventor： Leo Parker Dirac ,  Nicolle M. Correa ,  Aleksandr Mikhaylovich Ingerman ,  Sriram Krishnan ,  Jin Li ,  Sudhakar Rao Puvvadi ,  Saman Zarandioon

    IPC: G06N20/00

    Abstract: A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

    Machine learning service
    2.
    发明授权
    Machine learning service 有权

    公开(公告)号：US11386351B2

    公开(公告)日：2022-07-12

    申请号：US16159441

    申请日：2018-10-12

    Applicant: Amazon Technologies, Inc.

    Inventor： Leo Parker Dirac ,  Nicolle M. Correa ,  Aleksandr Mikhaylovich Ingerman ,  Sriram Krishnan ,  Jin Li ,  Sudhakar Rao Puvvadi ,  Saman Zarandioon

    IPC: G06N20/00 ,  G06N99/00

    Abstract: A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

    Machine learning service
    3.
    发明授权
    Machine learning service 有权

    公开(公告)号：US12073298B2

    公开(公告)日：2024-08-27

    申请号：US17811555

    申请日：2022-07-08

    Applicant: Amazon Technologies, Inc.

    Inventor： Leo Parker Dirac ,  Nicolle M. Correa ,  Aleksandr Mikhaylovich Ingerman ,  Sriram Krishnan ,  Jin Li ,  Sudhakar Rao Puvvadi ,  Saman Zarandioon

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

    Feature processing tradeoff management
    4.
    发明授权
    Feature processing tradeoff management 有权

    公开(公告)号：US11379755B2

    公开(公告)日：2022-07-05

    申请号：US16231124

    申请日：2018-12-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Leo Parker Dirac ,  Nicolle M. Correa ,  Charles Eric Dannaker

    IPC: G06N20/00 ,  H04L67/10

    Abstract: At a machine learning service, a set of candidate variables that can be used to train a model is identified, including at least one processed variable produced by a feature processing transformation. A cost estimate indicative of an effect of implementing the feature processing transformation on a performance metric associated with a prediction goal of the model is determined. Based at least in part on the cost estimate, a feature processing proposal that excludes the feature processing transformation is implemented.

    Machine learning service
    6.
    发明授权
    Machine learning service 有权

    公开(公告)号：US10102480B2

    公开(公告)日：2018-10-16

    申请号：US14319902

    申请日：2014-06-30

    Applicant: Amazon technologies, Inc.

    Inventor： Leo Parker Dirac ,  Nicolle M. Correa ,  Aleksandr Mikhaylovich Ingerman ,  Sriram Krishnan ,  Jin Li ,  Sudhakar Rao Puvvadi ,  Saman Zarandioon

    IPC: G06N99/00

    Abstract: A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

    FEATURE PROCESSING TRADEOFF MANAGEMENT
    10.
    发明申请
    FEATURE PROCESSING TRADEOFF MANAGEMENT 审中-公开

    公开(公告)号：US20190122136A1

    公开(公告)日：2019-04-25

    申请号：US16231124

    申请日：2018-12-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Leo Parker Dirac ,  Nicolle M. Correa ,  Charles Eric Dannaker

    IPC: G06N20/00

    Abstract: At a machine learning service, a set of candidate variables that can be used to train a model is identified, including at least one processed variable produced by a feature processing transformation. A cost estimate indicative of an effect of implementing the feature processing transformation on a performance metric associated with a prediction goal of the model is determined. Based at least in part on the cost estimate, a feature processing proposal that excludes the feature processing transformation is implemented.

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