Amazon Technologies, Inc.

Amir Sadoughi

Swaminathan Sivasubramanian

Stefano Stefani

Thomas Albert Faulhaber, Jr

Alexander Johannes Smola

Gowda Dayananda Anjaneyapura Range

Craig Wiley

Edo Liberty

Zohar Karnin

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 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

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New York, NY, United States of America

Amir Sadoughi