公开(公告)号：US11449797B1

公开(公告)日：2022-09-20

申请号：US16579744

申请日：2019-09-23

Applicant: Amazon Technologies, Inc.

Inventor： Kurniawan Kurniawan ,  Bhavesh A. Doshi ,  Umar Farooq ,  Patrick Ian Wilson ,  Vivek Bhadauria

IPC: G06N20/00 ,  H04L67/10

Abstract: An indication of training artifacts for a machine learning model to be trained with an input data set having an access restriction is obtained. A representation of a software execution environment containing the artifacts is deployed to a computing platform within an isolated resource group which satisfies the access restriction. A trained version of the machine learning model is generated at the computing platform, and transferred outside the isolated resource group.

公开(公告)号：US11373119B1

公开(公告)日：2022-06-28

申请号：US16369884

申请日：2019-03-29

Applicant: Amazon Technologies, Inc.

Inventor： Bhavesh A. Doshi ,  Anand Dhandhania

IPC: G06N20/00 ,  H04L41/16 ,  H04L47/70 ,  G06N5/04

Abstract: Techniques for a framework for building, orchestrating, and deploying complex, large-scale Machine Learning (ML) or deep learning (DL) inference applications is described. A ML application orchestration service is disclosed that enables the construction, orchestration, and deployment of complex ML inference applications in a provider network. The disclosed service provides customers with the ability to define machine learning (ML) models and define transformation operations on data before and/or after being provided to the ML models to construct a complex ML inference application. The service provides a framework for the orchestration (co-ordination) of the workflow logic (e.g., of the request and/or response flows) involved in building and deploying a complex ML inference application in the provider network.