Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Richard Shawn Bice

Subramanian Muralidhar

Stefano Stefani

Tate Andrew Certain

John Morgan Oslake

Jeffrey Robert Currier

Narayan Nevrekar

Kenneth Van Hyning

Swaminathan Sivasubramanian

Marvin Michael Theimer

Allan Henry Vermeulen

Douglas Brian Terry

Christopher Richard Jacques De Kadt

Alexander Evgenievich Nosov

Thomas Albert Faulhaber, Jr

Anthony A Virtuoso

Alexander Johannes Smola

Sameer Arun Verkhedkar

Murali Brahmadesam

Craig Wiley

Ramanathan Padinjarel Somanathan

Arijit Choudhury

Derek William Richard Chen-Becker

Taylor Goodhart

Tony Petrossian

Charles Drummond Swan

Rashmi Krishnaiah Setty

Edo Liberty

Steven Andrew Loeppky

Sanjay Shanthakumar

Sudhir Konduru

Ray Manpreet Singh Matharu

Abhijeet More

Murtaza Doctor

Suprio Pal

Jan Wieck

Philip Simko

Korry Allen Douglas

Asmita Barve

Sanket Vishram Chandorkar

Timothy Matthew Lewis

Hina Naseer

Karthikeyani Palanisami

Techniques for using scoring algorithms utilizing containers for flexible machine learning inference are described. In some embodiments, a request to host a machine learning (ML) model within a service provider network on behalf of a user is received, the request identifying an endpoint to perform scoring using the ML model. An endpoint is initialized as a container running on a virtual machine based on a container image and used to score data and return a result of said scoring to a user device.

Packaging and deploying algorithms for flexible machine learning

Techniques for implementing a multi-wire protocol and multi-dialect database engine are described. A database engine exposes multiple interfaces in the form of ports that support different database wire protocols. The database engine supports multiple query dialects that can be passed over any one of the supported wire protocols. The database engine can support multiple different query dialects within a single database session.

Multi-wire protocol and multi-dialect database engine for database compatability

Large amounts of human body movement data may be collected, possibly via streaming data, from one or more sensors worn by a user. The data may be analyzed along with other classification data to generate feedback for the user or for other interested people (e.g., a trainer, a coach, a team member, health professional, etc.). The analysis may utilize one or more machine learning (ML) algorithms that use training data to create one or more ML models. When a user is evaluated after receiving feedback, accuracy of the feedback may be evaluated and fed back to the ML model to continue training the ML model(s).

Streaming analytics of human body movement data

A shared resource service allows multiple clients to agree on rules for accessing a shared resource (e.g., a shared database or a shared service). The shared resource service also allows the clients to make changes to the rules (e.g., via consensus or majority vote). The clients use the shared resource service to enforce the rules, without having to trust each other to follow the rules when accessing the shared resource. First, the clients agree on a set of initial rules for accessing the shared resource and a set of initial rules for making changes to the rules. After the rules are initialized, then the clients can begin using the shared resource in accordance with the agreed upon rules. In response to a request for accessing the shared resource or a request for changing rules, the shared resource service enforce the applicable rules.

Agreement and enforcement of rules for a shared resource

A geographically distributed system using a hybrid, multi-master design is disclosed. The system may use global tables, for which both reads and writes may be performed locally, but with a single region serving as a master region. All strongly consistent reads and writes may be sent to the master region, whereas eventually consistent reads and writes may be performed in any region with a replica. The master region may not be statically assigned but may move from region to region. The master region may be assigned manually by client preference or may be assigned and updated automatically based on the time of day or workload characteristics.

Multi-region, multi-master replication of database tables

Semi-automatic failover includes automatic failover by a service provider as well as self-serviced failover by a service consumer. A signal can be afforded by a service provider based on analysis of an incident that affects the service provider. Initiation of self-serviced failover by a service consumer can be predicated on the signal. In one instance, the signal provides information that aids a decision of whether or not to failover. In another instance, the signal can grant or deny permission to perform a self-serviced failover.

Semi-automatic failover

Embodiments are directed to declaratively managing software applications, dynamically applying configuration changes to a specified software application based on a service level objective (SLO) and to efficiently performing an impact analysis for a specified service level agreement SLO.

Service level objective for cloud hosted applications

Growing community of inventors

Sammamish, WA, United States of America

Richard Shawn Bice