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公开(公告)号：US20200184380A1
公开(公告)日：2020-06-11
申请号：US16216138
申请日：2018-12-11
发明人： Gegi Thomas , Adelmo Cristiano Innocenza Malossi , Tejaswini Pedapati , Ganesh Venkataraman , Roxana Istrate , Martin Wistuba , Florian Michael Scheidegger , Chao Xue , Rong Yan , Horst Cornelius Samulowitz , Benjamin Herta , Debashish Saha , Hendrik Strobelt
摘要： A machine-learning model generation method, system, and computer program product deciding, via a first algorithm, a machine-learning algorithm that is best for customer data, invoking the machine-learning algorithm to train a neural network model with the customer data, analyzing the neural network model produced by the training for an accuracy, and improving the accuracy by iteratively repeating the training of the neural network model until a customer-defined constraint is met, as determined by the first algorithm.
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2.发明授权公开(公告)号：US11294759B2
公开(公告)日：2022-04-05
申请号：US16704083
申请日：2019-12-05
发明人： Evelyn Duesterwald , Punleuk Oum , Gaodan Fang , Debashish Saha , Anupama Murthi , Waldemar Hummer
摘要： A computer-implemented method includes obtaining data associated with execution of a model deployed in a computing environment. At least a portion of the obtained data is analyzed to detect one or more failure conditions associated with the model. One or more restoration operations are executed to generate one or more restoration results to address one or more detected failure conditions. At least a portion of the one or more restoration results is sent to the computing environment in which the model is deployed.
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公开(公告)号：US11429434B2
公开(公告)日：2022-08-30
申请号：US16724613
申请日：2019-12-23
发明人： Liana Fong , Seetharami R. Seelam , Ganesh Venkataraman , Debashish Saha , Punleuk Oum , Archit Verma , Prabhat Maddikunta Reddy
摘要： Embodiments relate to a system, program product, and method for supporting elastic execution of a machine learning (ML) workload using application based profiling. A joint profile comprised of both ML application execution and resource usage data is generated. One or more feature(s) and signature(s) from the joint profile are identified, and a ML execution model for ML application execution and resource usage is built. The ML execution model leverages the feature(s) and signature(s) and is applied to provide one or more directives to subsequent application execution. The application of the ML execution model supports and enables the ML execution to elastically allocate and request one or more resources from a resource management component, with the elastic allocation supporting application execution.
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4.发明申请公开(公告)号：US20210173736A1
公开(公告)日：2021-06-10
申请号：US16704083
申请日：2019-12-05
发明人： Evelyn Duesterwald , Punleuk Oum , Gaodan Fang , Debashish Saha , Anupama Murthi , Waldemar Hummer
摘要： A computer-implemented method includes obtaining data associated with execution of a model deployed in a computing environment. At least a portion of the obtained data are analyzed to detect one or more failure conditions associated with the model. One or more restoration operations are executed to generate one or more restoration results to address one or more detected failure conditions. At least a portion of the one or more restoration results is sent to the computing environment in which the model is deployed.
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公开(公告)号：US20230196178A1
公开(公告)日：2023-06-22
申请号：US17554166
申请日：2021-12-17
发明人： Benjamin Herta , Darrell Christopher Reimer , EVELYN DUESTERWALD , Gaodan Fang , Punleuk Oum , Debashish Saha , Archit Verma
CPC分类号： G06N20/00 , G06F9/4887 , G06F9/5038 , G06F9/38
摘要： A method of using a computing device to manage a lifecycle of machine learning models includes receiving, by a computing device, multiple pre-defined machine learning lifecycle tasks. The computing device manages executing a management-layer software layer for the multiple pre-defined machine learning lifecycle tasks. The computing device further generates and updates a machine learning pipeline using the management-layer software layer.
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公开(公告)号：US20210191759A1
公开(公告)日：2021-06-24
申请号：US16724613
申请日：2019-12-23
发明人： Liana Fong , Seetharami R. Seelam , Ganesh Venkataraman , Debashish Saha , Punleuk Oum , Archit Verma , Prabhat Maddikunta Reddy
摘要： Embodiments relate to a system, program product, and method for supporting elastic execution of a machine learning (ML) workload using application based profiling. A joint profile comprised of both ML application execution and resource usage data is generated. One or more feature(s) and signature(s) from the joint profile are identified, and a ML execution model for ML application execution and resource usage is built. The ML execution model leverages the feature(s) and signature(s) and is applied to provide one or more directives to subsequent application execution. The application of the ML execution model supports and enables the ML execution to elastically allocate and request one or more resources from a resource management component, with the elastic allocation supporting application execution.
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