Heterogeneous compute instance auto-scaling with reinforcement learning

    公开(公告)号:US11574243B1

    公开(公告)日:2023-02-07

    申请号:US16451878

    申请日:2019-06-25

    Abstract: Techniques for heterogeneous compute instance auto-scaling with reinforcement learning (RL) are described. A user specifies a reward function that generates rewards for use with an application simulation for determining what different instance types should be added to or removed from the application as part of training a RL model. The RL model can be automatically deployed and used to monitor an application to automatically scale the application fleet using heterogenous compute instances.

    SPLIT PREDICTIONS FOR IOT DEVICES

    公开(公告)号:US20210235543A1

    公开(公告)日:2021-07-29

    申请号:US17227194

    申请日:2021-04-09

    Abstract: A hub device of a network receives data from edge devices and generates a local result. The hub device also sends the data to a remote provider network and receives a result from the remote provider network, wherein the result is based on the data received from the edge devices. The hub device then generates a response based on the local result or the received result. The hub device may determine to correct the local result based on the result received from the remote provider network, and generate the response based on the corrected result. The hub device may generate an initial response before receiving the result from the provider network. For example, the hub device may determine that the confidence level for the local result is above the threshold level and in response, generate the initial response based on the local result.

    Systems and methods for knowledge transfer in machine learning

    公开(公告)号:US11620576B1

    公开(公告)日:2023-04-04

    申请号:US16908359

    申请日:2020-06-22

    Abstract: A training system may create and train a machine learning model with knowledge transfer. The knowledge transfer may transfer knowledge that is acquired by another machine learning model that has been previously trained to the machine learning model that is under training. The knowledge transfer may include a combination of representation transfer and instance transfer, the two of which may be performed alternatingly. The instance transfer may further include a filter mechanism to selectively identify instances with a satisfactory performance to implement the knowledge transfer.

    Training models for IOT devices
    8.
    发明授权

    公开(公告)号:US11108575B2

    公开(公告)日:2021-08-31

    申请号:US15660859

    申请日:2017-07-26

    Abstract: A model training service of a provider network receives data from edge devices of a remote network. The model training service analyzes the received data. The model training service may also analyze global data from other edge devices of other remote networks. The model training service may then generate updates to local data processing models based on the analysis. The updates are configured to update the local data processing models at the edge devices of the remote network. The provider network deploys the updates to the remote network. The updates are then applied to the data processing models of the edge devices.

    SIMULATION MODELING EXCHANGE
    9.
    发明申请

    公开(公告)号:US20200167687A1

    公开(公告)日:2020-05-28

    申请号:US16201864

    申请日:2018-11-27

    Abstract: A simulation application container executes a simulation of a system in a simulation environment, through which an agent representing the system uses a reinforcement learning model to operate within the simulation environment. The simulation application container obtains data indicating how the agent performed in the simulation environment and transmits this data to a robot application container. The robot application container uses the data to update the reinforcement learning model and provides the updated reinforcement learning model to perform another iteration of the simulation and continue training the reinforcement learning model.

    MODEL TIERING FOR IOT DEVICE CLUSTERS
    10.
    发明申请

    公开(公告)号:US20190037040A1

    公开(公告)日:2019-01-31

    申请号:US15660860

    申请日:2017-07-26

    Abstract: Edge devices of a network collect data. An edge device may determine whether to process the data using a local data processing model or to send the data to a tier device. The tier device may receive the data from the edge device and determine whether to process the data using a higher tier data processing model of the tier device. If the tier device determines to process the data, then the tier device processes the data using the higher tier data processing model, generates a result based on the processing, and sends the result to an endpoint (e.g., back to the edge device, to another tier device, or to a control device). If the tier device determines not to process the data, then the tier device may send the data on to another tier device for processing by another higher tier model.

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