Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Yue Zhao

Abhishek Kumar Singh

Avnish Kumar Chhabra

Huaming Huang

Muhammad Usman Sharif

Shuai Zheng

Li Zhang

Zheng Zhang

Sanjiv Ranjan Das

Siddharth Verma

Sheng Zha

Ash Beitz

Zhijiang He

Arbab Amjad

Xin Huang

Shenghua Yue

Abhishek Singh

Techniques for machine learning (ML) and natural language processing (NLP) are described. One technique enables the creation of a clean training dataset through just a few API calls. Another technique provides an automated process for generating a domain-specific lexicon, which is then used to generate ML training datasets, in a manner that requires little to no human labor. Another technique gathers ML training data from domain-specific public sources, which are more likely than typical public sources to contain focused terminology and to be free from errors, thus resulting in trained ML models that provide more accurate inferences.

Domain-specific language models

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

Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Safe and agile rollouts in a network-accessible server infrastructure using slices

Growing community of inventors

Redmond, WA, United States of America

Yue Zhao