Amazon Technologies, Inc.

Kartik Natarajan

Yi Zhang

Peng Zhang

John Baker

Ganesh Kumar Gella

Roger Scott Jenke

Meghana Puvvadi

Jason Krone

Santosh Kumar Ameti

Shang-Wen Daniel Li

Satya Naga Satis Kumar Gunuputi Alluri Venka

Ruhaab Markas

Shahab Shekari

Niranjhana Nayar

Garima Lalwani

Yi-An Lai

Techniques for updating a machine learning model based on user interactions are described. In particular, in some examples, user interactions with a chatbot provide aspects of a data set to be used to train or fine-tune a ML model. In some examples, this is accomplished by collecting data from a first plurality of interactions with a machine learning (ML) model; generating a variant of the ML model using the collected data by: filtering the collected data to create a first data set, training the ML model based on the first data set to generate an adapted ML model, and fine-tuning the adapted ML model on a second data set, different than the first data set to generate the variant of the ML model.

Adaptations of task-oriented agents using user interactions

Based on analysis of a workload associated with a throttling key of a client request directed to a first service, a scale-out requirement of the throttling key is obtained at respective resource managers of a plurality of other services which are utilized by the first service to respond to client requests. The resource managers initiate, asynchronously with respect to one another, resource provisioning tasks at each of the other services to fulfill the scale-out requirement. A throttling limit associated with the throttling key is updated to a second throttling key after the resource provisioning tasks are completed by the resource managers, and the updated limit is used to determine whether to accept another client request associated with the throttling key.

Automated provisioning techniques for distributed applications with independent resource management at constituent services

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Shoreline, WA, United States of America

Kartik Natarajan