Amazon Technologies, Inc.

Zohar Karnin

Stefano Stefani

Edo Liberty

Swaminathan Sivasubramanian

Leo Parker Dirac

Thomas Albert Faulhaber, Jr

Alexander Johannes Smola

Vladimir Zhukov

Gowda Dayananda Anjaneyapura Range

Tanya Bansal

Craig Wiley

Laurence Louis Eric Rouesnel

Orchid Majumder

Fan Li

Piali Das

Amir Sadoughi

Ravikumar Anantakrishnan Venkateswar

Patricia Grao Gil

Philip Gautier

Techniques for model variant generation using machine learning states are described. A machine learning state data structure is generated via an analysis of training data. The machine learning state can be used to quickly and efficiently generate multiple variants of a machine learning model without needing to re-analyze the raw training data again.

Efficient model generation using machine learning states

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 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

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Zohar Karnin