Amazon Technologies, Inc.

Vivek Bhadauria

Vasant Manohar

Joseph Patrick Tighe

Jonathan Hedley

Shubham Chandra Gupta

Gowtham Jeyabalan

Andrea Olgiati

Alessandro Bergamo

Prathyusha Reddy Cheruku

Stefano Stefani

Gaurav D Ghare

Rama Krishna Sandeep Pokkunuri

Roman Goldenberg

Dylan Chandler Thomas

Bhavesh Anil Doshi

Wei Xia

Ranju Das

Rakesh Madhavan Nambiar

Kalpesh N Sutaria

Anand Dhandhania

Enrico Sartorello

Nitin Singhal

Yong Yuan

Kunwar Yashraj Singh

Umar Farooq

Stephen Gould

Patrick Ian Wilson

Praveen Kumar Udayakumar

Mohammed El Hamalawi

Palak Mehta

Keith Young Johnson

Kuang Han

Renjun Zheng

Sean R Flynn

Kurniawan Kurniawan

Binglei Du

Paul Matthew Kohan

Michal Marshall

Ameer Nizarsami Ayoub

Karthik Ravindra Rao

Raghu Chaitanya Puligilla

Hammad Latif Mirza

Kshitiz Mohan Agarwal

Amulya Srivastava

Paul H Kang

Praveenkumar Udayakumar

A data set management of a provider network may allow a user to create new data set instances. When a data set instance is created, data set lineage metadata is also generated to describe the new data set instance, including the transformation that was applied to data in order to create the data set instance. When modifications are made to source data (e.g., a data bucket), then the modifications are propagated via transformations to the parent data set instance and to any child data set instances according to the data set lineage metadata in order to update the data set instances. When modifications are made to a parent data set instance to create an updated parent data set instance, then the modifications are propagated via transformations to any child data set instances according to the data set lineage metadata. Transformations and transformation patterns may also be defined and scheduled.

Data set management using data set lineage metadata

Techniques for unique label recognition and notification for streaming video. A video stream from a camera-equipped electronic device is received via the API endpoint of a stream processing service in a provider network. A request for unique label recognition and notification is received via an API endpoint of a computer vision service in the provider network. The computer vision service analyzes a video fragment of the video stream. If a unique label is recognized in the video fragment, then a notification service in the provider network sends a notification that the unique label was recognized in video captured by the camera-equipped electronic device.

Unique label recognition and notification for streaming video

Techniques for label recognition and notification for streaming video from non-overlapping cameras. A stream processing service of a provider network receives a first video stream from a first camera-equipped electronic device via an API endpoint of the stream processing service. The stream processing service also receives a second video stream from a second camera-equipped electronic device an API endpoint of the stream processing service. Meanwhile, a request for label recognition and notification is received at a computer vision service of the provider network via an API endpoint of the computer vision service. In response, the computer vision service recognizes a label in a video fragment of the first camera video stream and recognizes a label in a video fragment of the second camera video stream, and then identifies whether the two labels are the same label. If so, a notification service of the provider network sends a notification indicating that the label was recognized across non-overlapping cameras.

Label recognition and notification for streaming video from non-overlapping cameras

A network-based service may provide a machine learning model for different clients. The network-based service may implement an interface that allows a client to identify a test data set for validating versions of the machine learning model specifically for the client. When a new version of the machine learning model is created, a validation test using the test data set identified by the client may be used. Results of the validation test may be used to make a decision regard whether to migrate workloads for the client to the new version of the machine learning model.

Client-specific validation for migrating to a version of a machine learning model using client-supplied test data

An indication of training artifacts for a machine learning model to be trained with an input data set having an access restriction is obtained. A representation of a software execution environment containing the artifacts is deployed to a computing platform within an isolated resource group which satisfies the access restriction. A trained version of the machine learning model is generated at the computing platform, and transferred outside the isolated resource group.

Secure machine learning workflow automation using isolated resources

Objects detected in data may be filtered from an object recognition index. Data for object detection may be received. An object detection technique may be applied to the data to detect an object. If the object does not satisfy indexing criteria for the object recognition index, then the detected object may be excluded from the object recognition index.

Filtering detected objects from an object recognition index according to extracted features

A synthetic document generator that obtains a configuration for a synthetic document derived from real-world documents. The configuration specifies element templates to be included in the synthetic document and weights for the specified element templates. The system generates synthetic documents based on the configuration; the synthetic documents include diversified versions of the element templates specified in the configuration. Annotation documents are generated for the synthetic documents that include information describing the respective synthetic documents. A machine learning model for analyzing real-world documents can then be trained using the synthetic and annotation documents. Feedback from the analysis of real-world documents by the machine learning model can be used to generate a new configuration for generating additional synthetic and annotation documents which are used to further train the model.

Synthetic document generator

Techniques are described for automatically scaling (or 'auto scaling') compute resources&#x2014;for example, virtual machine (VM) instances, containers, or standalone servers&#x2014;used to support execution of service-oriented software applications and other types of applications that may process heterogeneous workloads. The resource requirements for a software application can be approximated by measuring 'worker pool' utilization of instances of each service, where a worker pool represents a number of requests that the service can process concurrently. A scaling service can thus be configured to scale the compute instances provisioned for a service in proportion to worker pool utilization, that is, compute instances can be added as the fleet's worker pools become more &#x201c;busy,&#x201d; while compute instances can be removed when worker pools become inactive.

Automatically scaling compute resources for heterogeneous workloads

Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.

Analysis of video content

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Redmond, WA, United States of America

Vivek Bhadauria