Amazon Technologies, Inc.

Pracheer Gupta

Poorna Chand Srinivas Perumalla

Stefano Stefani

Madan Mohan Rao Jampani

Andrea Olgiati

Jia Bi Zhang

Srikanth Kandalam Srinivasa

Maden Mohan Rao Jampani

A processing device receives a dataset comprising a plurality of data points, wherein each data point of the plurality of data points comprises a representative vector for the data point and an associated classification for the data point. The processing device determines, for the dataset, a score representative of a degree of clustering of the plurality of data points. The processing device determines a suitability of the dataset for use in machine learning based on the score.

Dataset suitability check for machine learning

Techniques are described for a nearest neighbor search service that enables users to perform nearest neighbor searches. The nearest neighbor search service includes an interface that enables users to create collections of searchable vectors, add and update vectors to a collection, delete vectors from a collection, and perform searches for nearest neighbors to a given vector. The nearest neighbor search service enables users to add, update, and delete vectors of a collection in real-time while also enabling users to perform searches at the same time.

Nearest neighbor search as a service

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

Incremental updates for nearest neighbor search

A computing resource service provider deploys resources to process input data sets on an ongoing basis and provide requestors with queryable data structures generated from the input data sets over determined, rolling periods of time. In one embodiment, the input data sets are processed using one or more nearest neighbor search algorithms, and the outputs therefrom are represented in data structures which are rotated as newer data structures are subsequently generated. The disclosed systems and techniques improve resource utilization, processing efficiency, query latency, and result consistency relative to known controls for large and/or complex data processing tasks, such as those employed in machine learning techniques.

Scalable distributed data processing and indexing

A method and system including a neural network configured to detect whether a source of the networks packets is transmitting in accordance with a recognized application protocol. The neural network analyzes a set of network packets to determine a probability that the network pattern corresponds to a network pattern associated with a recognized application protocol. If the probability associated with a first recognized application protocol exceeds a threshold probability value, the transmission of the set of network packets may be classified as being transmitted in accordance with the first recognized application protocol. If the probabilities corresponding to the respective recognized application protocols do not exceed the threshold probability value, the neural network classifies the transmission of the set of network packets as malware.

Growing community of inventors

Stanford, CA, United States of America

Pracheer Gupta