Amazon Technologies, Inc.

Kohen Berith Chia

Andrea Olgiati

Saurabh Gupta

Ramyanshu Datta

Lakshmi Naarayanan Ramakrishnan

Wei You

Arvind Sowmyan

Ishaaq Chandy

Kunal Mehrotra

Arun Babu Nagarajan

Zhihan Li

A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

Client-configurable retention periods for machine learning service-managed resources

Parameters of a pool of computing resources to be utilized for machine learning tasks from a set of entities are stored, including a category of the computing resources, and a post-task-completion retention period during which, after completion of a task, at least a portion of data stored at the resource is not to be deleted. A compute instance of the pool is assigned to a task requested from the set of entities after determining that one or more configuration settings of the instance satisfy a preference indicated in the request for the task, and that the retention period of the instance relative to a completion of an earlier task on the instance has not expired. A result of the task is stored.

Resource management techniques to reduce startup overhead for machine learning tasks

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Seattle, WI, United States of America

Kohen Berith Chia