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公开(公告)号：US11625269B1
公开(公告)日：2023-04-11
申请号：US17301343
申请日：2021-03-31
摘要： A technique for scheduling instructions includes obtaining a set of instructions that operate on memory objects, and determining the dependencies of the memory objects. The memory objects are then sorted into a sequence of memory objects based on the dependencies of the memory objects, and the set of instructions are scheduled into a sequence of instructions according to the sequence of memory objects. Sorting memory objects allows instructions that operate on the same memory object to be kept together. This helps minimize spilling conditions because intervening instructions that do not operate on the same memory object can be avoided.
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2.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
发明人： Thomas Albert Faulhaber, Jr. , Edo Liberty , Stefano Stefani , Zohar Karnin , Craig Wiley , Steven Andrew Loeppky , Swaminathan Sivasubramanian , Alexander Johannes Smola , Taylor Goodhart
摘要： 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：US11170309B1
公开(公告)日：2021-11-09
申请号：US15821564
申请日：2017-11-22
摘要： A machine learning model inference routing system in a machine learning service is described herein. The machine learning model inference routing system includes load balancer(s), network traffic router(s), an endpoint registry, and a feedback processing system that collectively allow the machine learning model inference routing system to adjust the routing of inferences based on machine learning model accuracy, demand, and/or the like. In addition, the arrangement of components in the machine learning model inference routing system enables the machine learning service to perform shadow testing, support ensemble machine learning models, and/or improve existing machine learning models using feedback data.
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公开(公告)号：US10831519B2
公开(公告)日：2020-11-10
申请号：US15901751
申请日：2018-02-21
发明人： Thomas Albert Faulhaber, Jr. , Gowda Dayananda Anjaneyapura Range , Jeffrey John Geevarghese , Taylor Goodhart , Charles Drummond Swan
摘要： Techniques for packaging and deploying algorithms utilizing containers for flexible machine learning are described. In some embodiments, users can create or utilize simple containers adhering to a specification of a machine learning service in a provider network, where the containers include code for how a machine learning model is to be trained and/or executed. The machine learning service can automatically train a model and/or host a model using the containers. The containers can use a wide variety of algorithms and use a variety of types of languages, libraries, data types, etc. Users can thus implement machine learning training and/or hosting with extremely minimal knowledge of how the overall training and/or hosting is actually performed.
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公开(公告)号：US12131188B1
公开(公告)日：2024-10-29
申请号：US18192081
申请日：2023-03-29
CPC分类号： G06F9/4881 , G06F8/43 , G06F8/433 , G06N3/063
摘要： A technique for scheduling instructions includes obtaining a set of instructions that operate on memory objects, and determining the dependencies of the memory objects. The memory objects are then sorted into a sequence of memory objects based on the dependencies of the memory objects, and the set of instructions are scheduled into a sequence of instructions according to the sequence of memory objects. Sorting memory objects allows instructions that operate on the same memory object to be kept together. This helps minimize spilling conditions because intervening instructions that do not operate on the same memory object can be avoided.
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公开(公告)号：US12045693B2
公开(公告)日：2024-07-23
申请号：US16001548
申请日：2018-06-06
发明人： Charles Drummond Swan , Edo Liberty , Steven Andrew Loeppky , Stefano Stefani , Alexander Johannes Smola , Swaminathan Sivasubramanian , Craig Wiley , Richard Shawn Bice , Thomas Albert Faulhaber, Jr. , Taylor Goodhart
CPC分类号： G06N20/00 , G06F9/45558 , G06F2009/45595
摘要： Techniques for using scoring algorithms utilizing containers for flexible machine learning inference are described. In some embodiments, a request to host a machine learning (ML) model within a service provider network on behalf of a user is received, the request identifying an endpoint to perform scoring using the ML model. An endpoint is initialized as a container running on a virtual machine based on a container image and used to score data and return a result of said scoring to a user device.
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公开(公告)号：US11550614B2
公开(公告)日：2023-01-10
申请号：US17067285
申请日：2020-10-09
发明人： Thomas Albert Faulhaber, Jr. , Gowda Dayananda Anjaneyapura Range , Jeffrey John Geevarghese , Taylor Goodhart , Charles Drummond Swan
摘要： Techniques for packaging and deploying algorithms utilizing containers for flexible machine learning are described. In some embodiments, users can create or utilize simple containers adhering to a specification of a machine learning service in a provider network, where the containers include code for how a machine learning model is to be trained and/or executed. The machine learning service can automatically train a model and/or host a model using the containers. The containers can use a wide variety of algorithms and use a variety of types of languages, libraries, data types, etc. Users can thus implement machine learning training and/or hosting with extremely minimal knowledge of how the overall training and/or hosting is actually performed.
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