Amazon Technologies, Inc.

Orchid Majumder

Leo Parker Dirac

Stefano Stefani

Vladimir Zhukov

Tanya Bansal

Laurence Louis Eric Rouesnel

Ravikumar Anantakrishnan Venkateswar

Patricia Grao Gil

Philip Gautier

Fan Li

Piali Das

Saurabh Gupta

Vineet Khare

Zohar Karnin

Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Automated machine learning pipeline exploration and deployment

Techniques for sequence to sequence (S2S) model building and/or optimization are described. For example, a method of receiving a request to build a sequence to sequence (S2S) model for a use case, wherein the request includes at least a training data set, generating parts of a S2S algorithm based on the at least one use case, determined parameters, and determined hyperparameters, and training a S2S algorithm built from the parts of the S2S algorithm using the training data set to generate the S2S model is detailed.

Building sequence to sequence (S2S) models using previously generated S2S models with similar use cases

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Bellevue, WA, United States of America

Orchid Majumder