Amazon Technologies, Inc.

Steven Andrew Loeppky

Stefano Stefani

Thomas Albert Faulhaber, Jr

Craig Wiley

Swaminathan Sivasubramanian

Edo Liberty

Alexander Johannes Smola

Taylor Goodhart

Leo Parker Dirac

Zohar Karnin

Aleksandr Mikhaylovich Ingerman

Richard Shawn Bice

Nicolle M Correa

Robert Matthias Steele

Saman Zarandioon

Tianming Zheng

Charles Drummond Swan

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 dynamic accuracy-based experimentation and deployment of machine learning (ML) models are described. Inference traffic flowing to ML models and the accuracy of the models is analyzed and used to ensure that better performing models are executed more often via model selection. A predictive component can evaluate which model is more likely to be accurate for certain input data elements. Ensemble techniques can combine inference results of multiple ML models to aim to achieve a better overall result than any individual model could on its own.

Dynamic accuracy-based deployment and monitoring of machine learning models in provider networks

Techniques for auto-scaling hosted machine learning models for production inference are described. A machine learning model can be deployed in a hosted environment such that the infrastructure supporting the machine learning model scales dynamically with demand so that performance is not impacted. The model can be auto-scaled using reactive techniques or predictive techniques.

Auto-scaling hosted machine learning models for production inference

A determination that a machine learning data set is to be shuffled is made. Tokens corresponding to the individual observation records are generated based on respective identifiers of the records' storage objects and record key values. Respective representative values are derived from the tokens. The observation records are rearranged based on a result of sorting the representative values and provided to a shuffle result destination.

Consistent sort-based record-level shuffling of machine learning data

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Seattle, WA, United States of America

Steven Andrew Loeppky