Amazon Technologies, Inc.

Safeer Mohiuddin

Akshat Vig

Rama Krishna Sandeep Pokkunuri

Rajaprabhu Thiruchi Loganathan

Gopinath Duddi

Parikshit Shivajirao Pol

Subramanian Sankara Subramanian

Sudarshan Narasimhan

Poorna Chand Srinivas Perumalla

Madan Mohan Rao Jampani

Nagajyothi Nookula

Eduardo Manuel Calleja

Aashish Jindia

Vinay Hanumaiah

Romi Boimer

Song Ge

Wanqiang Chen

Fei Chen

Danjuan Ye

Rama Krishna Pokkunuri

Techniques for generating and executing an execution plan for a machine learning (ML) model using one of an edge device and a non-edge device are described. In some examples, a request for the generation of the execution plan includes at least one objective for the execution of the ML model and the execution plan is generated based at least in part on comparative execution information and network latency information.

Automated distribution of models for execution on a non-edge device and an edge device

Information describing changes to a collection of items maintained by a database may be stored in a log file. The information in the log file may be converted into a stream of records describing the changes. The records may be directed to a computing node selected for performing a trigger function in response to the change, based on applying a hash function to a portion of the record, identifying a hash space associated with a value output by the hash function, and mapping from the hash space to the selected computing node.

Distributed stream-based database triggers

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Saratoga, CA, United States of America

Safeer Mohiuddin