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公开(公告)号：US12277480B1
公开(公告)日：2025-04-15
申请号：US15934091
申请日：2018-03-23
Applicant: Amazon Technologies, Inc.
Inventor： Edo Liberty , Thomas Albert Faulhaber, Jr. , Zohar Karnin , Gowda Dayananda Anjaneyapura Range , Amir Sadoughi , Swaminathan Sivasubramanian , Alexander Johannes Smola , Stefano Stefani , Craig Wiley
Abstract: Techniques for in-flight scaling of machine learning training jobs are described. A request to execute a machine learning (ML) training job is received within a provider network, and the ML training job is executed using a first one or more compute instances. Upon a determination that a performance characteristic of the ML training job satisfies a scaling condition, a second one or more compute instances are added to the ML training job while the first one or more compute instances continue to execute portions of the ML training job.
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公开(公告)号：US12061963B1
公开(公告)日：2024-08-13
申请号：US18340561
申请日：2023-06-23
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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Patent Agency Ranking
公开(公告)号：US11449798B2
公开(公告)日：2022-09-20
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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4.发明授权公开(公告)号：US11257002B2
公开(公告)日：2022-02-22
申请号：US15919628
申请日：2018-03-13
Applicant: Amazon Technologies, Inc.
Inventor： Thomas Albert Faulhaber, Jr. , Edo Liberty , Stefano Stefani , Zohar Karnin , Craig Wiley , Steven Andrew Loeppky , Swaminathan Sivasubramanian , Alexander Johannes Smola , Taylor Goodhart
Abstract: Techniques for dynamic accuracy-based experimentation and deployment of machine learning (ML) models are described. Inference traffic flowing to ML models and the accuracy of the models is analyzed and used to ensure that better performing models are executed more often via model selection. A predictive component can evaluate which model is more likely to be accurate for certain input data elements. Ensemble techniques can combine inference results of multiple ML models to aim to achieve a better overall result than any individual model could on its own.
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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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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：US11537439B1
公开(公告)日：2022-12-27
申请号：US15934046
申请日：2018-03-23
Applicant: Amazon Technologies, Inc.
Inventor： Edo Liberty , Thomas Albert Faulhaber, Jr. , Zohar Karnin , Gowda Dayananda Anjaneyapura Range , Amir Sadoughi , Swaminathan Sivasubramanian , Alexander Johannes Smola , Stefano Stefani , Craig Wiley
Abstract: Techniques for intelligent compute resource selection and utilization for machine learning training jobs are described. At least a portion of a machine learning (ML) training job is executed a plurality of times using a plurality of different resource configurations, where each of the plurality of resource configurations includes at least a different type or amount of compute instances. A performance metric is measured for each of the plurality of the executions, and can be used along with a desired performance characteristic to generate a recommended resource configuration for the ML training job. The ML training job is executed using the recommended resource configuration.
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