Amazon Technologies, Inc.

Google Inc.

Alexander Johannes Smola

Craig Wiley

Swaminathan Sivasubramanian

Stefano Stefani

Thomas Albert Faulhaber, Jr

Edo Liberty

Vineet Khare

Mu Li

Amr Ahmed

Zohar Karnin

Gowda Dayananda Anjaneyapura Range

Taylor Goodhart

Steven Andrew Loeppky

Amir Sadoughi

Andrew Stephen Tomkins

Sudipta Sengupta

Eugene Jon Shekita

Marc Alexander Najork

Vanja Josifovski

Jie Yang

Rama Krishna Sandeep Pokkunuri

Bing Xiang

Ramesh M Nallapati

Stefano Soatto

Mike Bendersky

Luis Garcia Pueyo

MyLinh Yang

Bhavin Thaker

Hui Tan

Amitabh Saikia

Marc-Allen Cartright

Daniel Joseph Clancy

Madan Mohan Rao Jampani

Parminder Bhatia

Atul Deo

Richard Shawn Bice

Tim Januschowski

Andrew Oliver Arnold

Charles Drummond Swan

Matthew Lee

Daniel Wyatt

Srinivas Iragavarapu

Bor-Yiing Su

Ankur Deepak Desai

Naveen Mysore Nagendra Swamy

Yuyang Wang

Qiang Yu

Leyuan Wang

Tatsuya Arai

Rasool Fakoor

Pratik Anil Chaudhari

Hang Zhang

Nick Dean Erickson

Yohei Nakayama

Ryan Matthew Brand

Abhi Vinayaka Sharma

Dean P Foster

Jonas Mueller

Martin Zinkevich

Weinan Zhang

Wasi Uddin Ahmad

Sathish Arumugam Selvaraj

Venkat Rakshith Reddy Swamireddy

Lei Gao

Wei Fang

Code completion suggestions may be proactively obtained and validated. An event that triggers obtaining a code completion suggestion for inclusion in a code file being edited using an integrated development environment may be detected. The code completion suggestion may be obtained. The characters of the code completion suggestion may be compared with characters added to the code file after the detection of the event that triggered obtaining the code completion suggestion to determine whether the code completion suggestion is valid. A valid code completion suggestion may then be displayed.

Validating and providing proactively generated code completion suggestions for code development

Techniques for in-flight scaling of machine learning training jobs are described. A request to execute a machine learning (ML) training job is received within a provider network, and the ML training job is executed using a first one or more compute instances. Upon a determination that a performance characteristic of the ML training job satisfies a scaling condition, a second one or more compute instances are added to the ML training job while the first one or more compute instances continue to execute portions of the ML training job.

In-flight scaling of machine learning training jobs

Techniques for code-free automated machine learning (ML) are described. Users can train high-quality ML models and pipelines without necessarily needing to write code by providing a training dataset to a code-free machine learning service. The service may deploy an ML orchestration function and a storage location on behalf of a user. When a modification is made to the storage bucket, such as by the user providing a training dataset, the orchestration function is invoked and can automatically initiate an AutoML process using at least the training data to train multiple ML model variants. The resultant ML model(s) and associated metrics can be provided to the user, deployed behind an endpoint, and/or used to generate inferences.

Code-free automated machine learning

Techniques for Meta-Q-Learning (MQL) are described. A method of MQL may include receiving a request from an agent to perform adaptation based at least on task data associated with a new task collected by the agent, identifying a subset of meta-training data corresponding to the task data in a replay buffer, and adapting a policy using the subset of meta-training data and the task data to generate an adapted policy, wherein the adapted policy is used identify a next action for the agent to perform.

Meta-Q learning

Techniques for providing and servicing listed repository items such as algorithms, data, models, pipelines, and/or notebooks are described. In some examples, web services provider receives a request for a listed repository item from a requester, the request indicating at least a category of the repository item and each listing of a repository item includes an indication of a category that the listed repository item belongs to and a storage location of the listed repository item, determines a suggestion of at least one listed repository item based on the request, and provides the suggestion of the at least one listed repository item to the requester.

Machine learning repository service

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.

Packaging and deploying algorithms for flexible machine learning

Techniques for forecasting using deep factor models with random effects are described. A forecasting framework combines the strengths of both classical and neural forecasting methods in a global-local framework for forecasting multiple time series. A global model captures the common latent patterns shared by all time series, while a local model explains the variations at the individual level.

Deep factor models with random effects for forecasting

Techniques for intelligent compute resource selection and utilization for machine learning training jobs are described. At least a portion of a machine learning (ML) training job is executed a plurality of times using a plurality of different resource configurations, where each of the plurality of resource configurations includes at least a different type or amount of compute instances. A performance metric is measured for each of the plurality of the executions, and can be used along with a desired performance characteristic to generate a recommended resource configuration for the ML training job. The ML training job is executed using the recommended resource configuration.

Intelligent compute resource selection for machine learning training jobs

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.

Dynamic accuracy-based deployment and monitoring of machine learning models in provider networks

Techniques for determining and utilizing optimal aggregation schedules are described are described. A deep machine learning model can be trained using multiple processing elements implemented in one or multiple computing devices and that are interconnected using one or multiple types of links. An optimal aggregation schedule for such arbitrary topologies can be determined automatically. The determination may include solving a linear program on the spanning tree polytope. The optimal aggregation schedule can be utilized by the multiple processing elements to train the deep machine learning model.

Optimal message scheduling for aggregation

Methods and apparatus related to determining an activity of a user based on sensor readings from sensor(s), and providing, for presentation to the user via a user interface output of a computing device of the user, information that is based on the determined activity. In some implementations, the information may be provided in response to input entered by the user via a user interface input device of the computing device of the user. In some implementations, the input may be a search query and the information may be search results. In some implementations, the input may be a partial query and the information may be query suggestions.

Providing query suggestions and/or search results based on determined user activity

Methods, apparatus, and computer-readable media are provided for analyzing a cluster of communications, such as B2C emails, to generate a template for the cluster that defines transient segments and fixed segments of the cluster of communications. More particularly, methods, apparatus, and computer-readable media are provided for generating and/or applying a trained structured machine learning model for a generated template that can be used to determine, for one or more transient segments of subsequent communications, a corresponding probability that a given semantic label is the correct semantic label for extracted content of the transient segment(s).

Generating and applying a trained structured machine learning model for determining a semantic label for content of a transient segment of a communication

A cluster formation engine invokes generation of an automatically scalable group (ASG) of virtual machine instances, where the ASG is associated with one or more applications to be run in a cloud computing environment. The cluster formation engine detects a failure to generate a first virtual machine instance to be included in the ASG, and completes the generation of the ASG without including the first virtual machine instance in the ASG.

Virtual machine cluster formation using available instances

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Sunnyvale, CA, United States of America

Alexander Johannes Smola