Amazon Technologies, Inc.

Weixun Wang

Arun Babu Nagarajan

Gregory Branchek Roth

Graeme David Baer

Jasmeet Chhabra

Alazel Acheson

Daniel Stephen Popick

Dmitry Frenkel

Ankit Kumar

Luke Edward Kennedy

Owen Thomas

Urvashi Chowdhary

Bradford Taylor Lyman

Kenneth O Henderson

Uday Kumar Bandaru

Dipankar Patro

Zhankui Lu

Khushboo Srivastava

Brian Ellison Granger

Manoj Ravi

Techniques described herein may be implemented in the context of a computing resource service provider. A machine learning (ML) service provides an interface to clients which can be used to create, read, update, and delete ML pipelines. ML pipelines can be converted to a human-readable format, which a server persists. Clients of a machine learning service can start, stop, and resume executions a ML pipeline based on the human-readable format of the ML pipeline.

Machine learning pipelines

Techniques for remote computing are described. In some examples, a decorator call in source code is used to generate a remote job to be processed by a provider network using a runtime environment that replicates a runtime environment where the source code was developed. The runtime environment is replicated using information such as runtime dependencies, packages, and environment variables of the development environment.

Local runtime replication for machine learning model training on a provider network

The updating of a definition layer or schema for a large distributed database can be accomplished using a plurality of data store tiers. A distributed database can be made up of many individual data stores, and these data stores can be allocated across a set of tiers based on business logic or other allocation criteria. The update can be applied sequentially to the individual tiers, such that only data stores for a single tier are being updated at any given time. This can help to minimize downtime for the database as a whole, and can help to minimize problems that may result from an unsuccessful update. Such an approach can also allow for simplified error detection and rollback, as well as providing control over a rate at which the update is applied to the various data stores of the distributed database.

Multi-tier definition management for distributed data stores

A load balancing service receives a request from a customer to configure a load balancer for distributing incoming requests to one or more computing resources of the customer. The load balancing service uses configuration information specified in the request to configure the load balancer such that the load balancer, in response to an incoming request, can determine whether the incoming request satisfies a set of request processing rules usable to determine whether the request is to be transmitted to any computing resource of the one or more computing resources. The load balancer transmits the incoming request to a computing resource of the customer as a result of the rules being satisfied.

Load balancer request processing

Growing community of inventors

Sammamish, WA, United States of America

Weixun Wang