Amazon Technologies, Inc.

Jia Bi Zhang

Stefano Stefani

Andrea Olgiati

Poorna Chand Srinivas Perumalla

Anthony Joseph Suarez

Madan Mohan Rao Jampani

Pracheer Gupta

Srikanth Kandalam Srinivasa

Jonathan Hedley

Aakash Deep

Meng Wang

Anirudh Balachandra Aithal

Kiran Kumar Meduri

Christopher Brian Barclay

Yuanjun Xiong

Cornelle Christiaan Pretorius Janse Van Rensburg

Fernando Paiva Zandona

Mengye Gong

Juan Pablo Escalona Garcia

Marcel Jon Van Rensberg

Bing Shuai

Vamsi Krishna Giridharan

Ming Xie

A first encoding representing a set of detected signals is obtained at a sensor-proximity resource of an object recognition application which also includes resources of an analytics service of a provider network. In response to a determination that a cache at the sensor-proximity resource does not include a second encoding which satisfies a similarity criterion with respect to the first encoding, at least a portion of a partition of a spatial index is obtained from another resource selected using an index partition map. A recognition-based action is initiated based on determining that the partition includes an encoding which satisfies the similarity criterion.

Distributed system for efficient entity recognition

A machine learning model, such as a neural network, is partially encrypted with a homomorphic encryption scheme. Application of the machine learning model to data includes performing operations on plaintext and encrypting results of operations for input to other operations that are performed over cyphertext. Ciphertext output of such operations can be provided to a service that is able to decrypt the ciphertext output.

Encrypted machine learning models

A search management system and method to perform a search of first set of records maintained in a transactional data store (e.g., a commit log of a relational data store) and a second set of records maintained in a batch-updated data store. The search results corresponding to the transactional data store and the batch-updated data store are merged to generate a search result dataset to provide in response to a search query. The transactional layer or transaction data store is a commit log configured to manage and store records in accordance with recently executed operations (e.g., add and delete record operations) based on communications with one or more customer systems relating to customer data. Records maintained in the commit log are subsequently stored in the batch-updated data store as a result of a batching process.

Transactional and batch-updated data store search

A data set may be partitioned according to relative differences indicated by a cover tree. A cover tree may be generated for a data set. Items in the data set may be stored at the same or different nodes according to the relative difference between the items indicated in the cover tree. Portions of the cover tree may be assigned to different nodes storing the data set. Access requests for the data set may be performed by sending the access requests to nodes identified according to the assigned portions of the cover tree.

Partitioning data according to relative differences indicated by a cover tree

An event applier service obtains state change information for a plurality of software containers from a transaction journal. Based at least in part on operations specified in the state change information for the plurality of software containers, the event applier service generates event data for the plurality of containers. The event applier service updates a database to indicate that the state change information has been processed to generate the event data. The event applier service provides the event data.

Software container event monitoring systems

A method and system for processing images by a neural network are provided. A first computer can sequentially transfer at least two versions of an image having increasingly greater resolution to a second computer. The second computer performs an image identification process on each of the sequentially transferred at least two versions until the image is identified.

Streaming-based deep learning models for computer vision tasks

At least one instance of an application is launched in a set of software containers that are distributed among a set of virtual machine instances. A set of measurements corresponding to resource utilization by a software container of the set of software containers is obtained and a timestamp is generated for the set of measurements. The set of measurements is aggregated, with other sets of measurements corresponding to the set of software containers for the application, into a set of aggregated measurements grouped in a time window group, based at least in part on the timestamp, and, as a result of fulfillment of a condition, the time window group is outputted.

Container telemetry

A processing device receives a representation of an image, wherein the image has a first size and the representation has a second size that is smaller than the first size, the representation having been generated from the image by a first portion of a first trained machine learning model. The processing device processes the representation of the image using a second portion of the trained machine learning model to generate a reconstruction of the image and then outputs the reconstruction of the image.

Image compression and decompression using embeddings

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Seattle, WA, United States of America

Jia Bi Zhang