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公开(公告)号：US10891152B2
公开(公告)日：2021-01-12
申请号：US15360818
申请日：2016-11-23
Applicant: Amazon Technologies, Inc.
Inventor： Vikram Sathyanarayana Anbazhagan , Swaminathan Sivasubramanian , Stefano Stefani , Vladimir Zhukov
Abstract: A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Based on received input, a first service of a plurality of services is identified. The first service is to be used to perform a first task associated with the first parameter. Portions of the first application to determine the value of the first parameter and to invoke the first service are generated.
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公开(公告)号：US12039415B2
公开(公告)日：2024-07-16
申请号：US16588913
申请日：2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Andrea Olgiati , Lakshmi Naarayanan Ramakrishnan , Jeffrey John Geevarghese , Denis Davydenko , Vikas Kumar , Rahul Raghavendra Huilgol , Amol Ashok Lele , Stefano Stefani , Vladimir Zhukov
Abstract: Methods, systems, and computer-readable media for debugging and profiling of machine learning model training are disclosed. A machine learning analysis system receives data associated with training of a machine learning model. The data was collected by a machine learning training cluster. The machine learning analysis system performs analysis of the data associated with the training of the machine learning model. The machine learning analysis system detects one or more conditions associated with the training of the machine learning model based at least in part on the analysis. The machine learning analysis system generates one or more alarms describing the one or more conditions associated with the training of the machine learning model.
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公开(公告)号：US11727314B2
公开(公告)日：2023-08-15
申请号：US16587301
申请日：2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Tanya Bansal , Piali Das , Leo Parker Dirac , Fan Li , Zohar Karnin , Philip Gautier , Patricia Grao Gil , Laurence Louis Eric Rouesnel , Ravikumar Anantakrishnan Venkateswar , Orchid Majumder , Stefano Stefani , Vladimir Zhukov
CPC classification number: G06N20/20 , G06F9/5066 , G06F9/546
Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.
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公开(公告)号：US10460728B2
公开(公告)日：2019-10-29
申请号：US15625942
申请日：2017-06-16
Applicant: Amazon Technologies, Inc.
Inventor： Vikram Sathyanarayana Anbazhagan , Swaminathan Sivasubramanian , Stefano Stefani , Vladimir Zhukov
Abstract: Methods, systems, and computer-readable media for exporting dialog-driven applications to digital communication platforms are disclosed. A launch condition is received from a user. The launch condition is caused to be registered with one or more digital communication platforms. Detection of the launch condition is to cause a natural language input to be routed from at least one of the digital communication platforms to an application management service.
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公开(公告)号：US20210097444A1
公开(公告)日：2021-04-01
申请号：US16587301
申请日：2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Tanya BANSAL , Piali DAS , Leo Parker DIRAC , Fan LI , Zohar KARNIN , Philip GAUTIER , Patricia GRAO GIL , Laurence Louis Eric ROUESNEL , Ravikumar Anantakrishnan VENKATESWAR , Orchid MAJUMDER , Stefano Stefani , Vladimir Zhukov
Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.
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Patent Agency Ranking
公开(公告)号：US10331791B2
公开(公告)日：2019-06-25
申请号：US15360814
申请日：2016-11-23
Applicant: Amazon Technologies, Inc.
Inventor： Vikram Sathyanarayana Anbazhagan , Rama Krishna Sandeep Pokkunuri , Swaminathan Sivasubramanian , Stefano Stefani , Vladimir Zhukov
IPC: G06F17/27 , G10L15/22 , G06N20/00 , G06F8/30 , G10L15/183
Abstract: A natural language understanding model is trained using respective natural language example inputs corresponding to a plurality of applications. A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Using the natural language understanding model, at least a portion of the first application is generated.
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公开(公告)号：US20180366114A1
公开(公告)日：2018-12-20
申请号：US15625942
申请日：2017-06-16
Applicant: Amazon Technologies, Inc.
Inventor： Vikram Sathyanarayana Anbazhagan , Swaminathan Sivasubramanian , Stefano Stefani , Vladimir Zhukov
Abstract: Methods, systems, and computer-readable media for exporting dialog-driven applications to digital communication platforms are disclosed. A launch condition is received from a user. The launch condition is caused to be registered with one or more digital communication platforms. Detection of the launch condition is to cause a natural language input to be routed from at least one of the digital communication platforms to an application management service.
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公开(公告)号：US12008390B1
公开(公告)日：2024-06-11
申请号：US16699488
申请日：2019-11-29
Applicant: Amazon Technologies, Inc.
Inventor： Thomas Albert Faulhaber , Jonathan Esterhazy , Vladimir Zhukov , Stefano Stefani
CPC classification number: G06F9/45558 , G06F9/54 , G06F2009/45562 , G06F2009/45591
Abstract: Methods and apparatus for providing persistent execution environments for computation systems including but not limited to interactive computation systems. A service is provided that extends the notion of static containers to dynamically changing execution environments into which users can install code, add files, etc. The execution environments are monitored, and changes to an execution environment are automatically persisted to environment versions(s) so that code run in the execution environment can be run later or elsewhere simply by referring to the environment. There is no explicit build step for the user. Instead, incremental changes are added to environment versions which are stored and are ready to be used to instantiate respective execution environments on other compute instances.
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公开(公告)号：US20210132986A1
公开(公告)日：2021-05-06
申请号：US17145133
申请日：2021-01-08
Applicant: Amazon Technologies, Inc.
Inventor： Vikram Sathyanarayana Anbazhagan , Swaminathan Sivasubramanian , Stefano Stefani , Vladimir Zhukov
Abstract: A determination is made as to whether a value of a first parameter of a first application is to be obtained using a natural language interaction. Based on received input, a first service of a plurality of services is identified. The first service is to be used to perform a first task associated with the first parameter. Portions of the first application to determine the value of the first parameter and to invoke the first service are generated.
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公开(公告)号：US20210097433A1
公开(公告)日：2021-04-01
申请号：US16588952
申请日：2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Andrea Olgiati , Maximiliano Maccanti , Arun Babu Nagarajan , Lakshmi Naarayanan Ramakrishnan , Urvashi Chowdhary , Gowda Dayananda Anjaneyapura Range , Zohar Karnin , Laurence Louis Eric Rouesnel , Stefano Stefani , Vladimir Zhukov
Abstract: Methods, systems, and computer-readable media for automated problem detection for machine learning models are disclosed. A machine learning analysis system receives data associated with use of a machine learning model. The data was collected by a machine learning inference system and comprises input to the model or a plurality of inferences representing output of the machine learning model. The machine learning analysis system performs analysis of the data associated with the use of the machine learning model. The machine learning analysis system detects one or more problems associated with the use of the machine learning model based at least in part on the analysis. The machine learning analysis system initiates one or more remedial actions associated with the one or more problems associated with the use of the machine learning model.
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