Amazon Technologies, Inc.

Owen Thomas

Thomas Albert Faulhaber, Jr

Vladimir Zhukov

Glenn Danthi

Sumit Thakur

Hugh Payton Staub

Kenneth O Henderson

Stefano Stefani

Arun Babu Nagarajan

Weixun Wang

Kenneth Oliver Henderson, Jr

Urvashi Chowdhary

Artifacts, including parameters are data sets, associated with experiment tasks are stored at an experiment management service. A query specifying a particular value of a parameter and a particular data set is received, and an indication of an experiment result associated with the particular data set and the particular parameter value is provided.

Experiment management service

Techniques described herein may be implemented in the context of a computing resource service provider. A machine learning (ML) service provides an interface to clients which can be used to create, read, update, and delete ML pipelines. ML pipelines can be converted to a human-readable format, which a server persists. Clients of a machine learning service can start, stop, and resume executions a ML pipeline based on the human-readable format of the ML pipeline.

Machine learning pipelines

A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.

Network-accessible machine learning model training and hosting system

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Seattle, WA, United States of America

Owen Thomas