Accenture Global Solutions Limited

Andrew Nam

Teresa Sheausan Tung

Mohamad Mehdi Nasr-Azadani

Zaid Tashman

Matthew Bernard Kujawinski

Nicholas Akiona

Yao A Yang

Matthew Lee Austin

Mohamad Mehdi Nasr-azadani

Thien Quang Nguyen

Ophelia Min Zhu

Yao Yang

Ruiwen Li

A resource prediction system for executing machine learning models and method are provided. The system includes non-transitory memory storing instructions and a processor configured to execute the instructions to obtain input data including a targeted objective and the constraints, select a deployable machine learning model having an evaluation score that meets a predetermined criterion from among candidate machine learning models, virtually execute the deployable machine learning model on each of candidate hardware platforms according to the constraints, generate an assessment report of the virtual performance metrics set of the deployable machine learning model executed on each of the candidate hardware platforms, and select the suggested hardware platform meeting the predetermined criterion from among the candidate hardware platforms.

Resource prediction system for executing machine learning models

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support cross-server containerized application allocation, multi-tenant namespace management, and data layer deployment in an edge environment. To illustrate, containers associated with an application are deployed to edge servers based on a geographic characteristic of the edge server with respect to an edge device receiving services from the application. A common data layer is provided across the edge environment to manage communications between the different containers, and between the application and other applications of the edge environment. Managing the communication is based on namespaces and/or a modality (e.g., private or public) associated with the application. Authentication configuration of the application is used to determine edge resource access for the containers of the application. A common orchestration layer is provided across the edge environment to manage scaling and configuration updates of the application.

Scalable, robust, and secure multi-tenant edge architecture for mission-critical applications

This disclosure is directed to a generalizable machine learning model production environment and system with a defense mechanism that facilitates safe execution of machine learning models in production by effectively detecting potential known and new adversarial attacks. The disclosed exemplary systems and architectures gather data from the online execution of the machine learning models and communicate with an on-demand pipelines for further inspection and/or correction of vulnerabilities in the production machine learning model to the detected attacks. These systems and architectures provide an automatable process for continuous monitoring of model performance and correction of the production machine learning model to guard against current and future adversarial attacks.

Machine learning model robustness against adversarial attacks in production

The present disclosure relates to a system, a method, and a product for optimizing hyper-parameters for generation and execution of a machine-learning model under constraints. The system includes a memory storing instructions and a processor in communication with the memory. When executed by the processor, the instructions cause the processor to obtain input data and an initial hyper-parameter set; for an iteration, to build a machine learning model based on the hyper-parameter set, evaluate the machine learning model based on the target data to obtain a performance metrics set, and determine whether the performance metrics set satisfies the stopping criteria set. If yes, the instructions cause the processor to perform an exploitation process to obtain an optimal hyper-parameter set, and exit the iteration; if no, perform an exploration process to obtain a next hyper-parameter set, and perform a next iteration with using the next hyper-parameter set as the hyper-parameter set.

Resource-aware automatic machine learning system

Growing community of inventors

San Francisco, CA, United States of America

Andrew Nam