Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Siddharth Verma

Abhishek Kumar Singh

Avnish Kumar Chhabra

Huaming Huang

Muhammad Usman Sharif

Yue Zhao

Pradeep Ramesha

Hasamatulla Baig Mirja Mahammad

Sridhar Komandur

Ash Beitz

Afreen Afreen

Arbab Amjad

Gaurav Khandelwal

Techniques for incorporating machine learning recommendations into presentation of network information within predefined load times are described herein. For example, a computer system can determine, based on a selection via a user interface of a client device presenting first network information, a request for a second network information. The computer system can cause a processor to use a machine learning model to generate a first recommendation based on contextual data and indicating a first parameter to present the second network information. The computer system may access a pre-computed second recommendation based on the contextual data. The second recommendation indicates a second parameter. The computer system can cause of the client device to present the second network information such that the second network information is presented by at least using the first parameter or the second parameter based on a response time for the machine learning model generating the first parameter.

Incorporating machine learning recommendations into presentation of network information

Methods, systems, apparatuses, and computer program products are provided that enable the automated deployment of microservices to a network-accessible server set. The automated deployment may be based on constraint(s) that are specified by a declarative deployment model that is associated with the microservice to be deployed. For example, a centralized deployment orchestrator may receive microservice(s) and their associated declarative deployment model(s). The deployment orchestrator analyzes the declarative deployment model(s) and determines which microservice(s) are to be deployed based on the constraint(s) specified by the declarative deployment model(s). The foregoing techniques advantageously determine when to deploy microservice(s), while also minimizing human intervention typically required to deploy microservice(s). Moreover, by controlling when certain microservice(s) are deployed and/or which microservice(s) are deployed, deployed microservice(s) that function incorrectly and/or cause at least a portion of the network-accessible computing environment to malfunction can be easily identified.

Automated constraint-based deployment of microservices to cloud-based server sets

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Seattle, WA, United States of America

Siddharth Verma