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NTT DATA is a leading business and IT services provider and global innovation partner with 80,000 professionals based in more than 40 countries.
whitePaper | January 6, 2022
This document outlines the principles behind ControlUp’s technical and organizational data security measures. These measures are provided as standard to all customers and in all ControlUp products and services, as required by the Regulation (EU 2016/679), the General Data Protection Regulation ("GDPR").
whitePaper | May 23, 2022
Demand for cloud-based applications is growing in both the consumer and business spheres. Hybrid cloud computing combines private and public cloud resources. This model will radically change the design and management of corporate networks – as hybrid cloud computing also requires a hybrid network infrastructure.
whitePaper | September 25, 2021
This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle. Your access to and use of this confidential material is subject to the terms and conditions of your Oracle software license and service agreement, which has been executed and with which you agree to comply.
whitePaper | June 15, 2022
According to IDC, the Dedicated Cloud Infrastructure-as-aService (DCIaaS) market will grow revenues by more than 100x over the next five years. In another IDC survey, 61% of enterprise respondents indicated that they have migrated workloads to dedicated cloud services or onpremises infrastructure.
whitePaper | March 3, 2021
Scality has created ARTESCA to solve the storage and management problems of new content-rich data workloads in a cloud-native application world.
whitePaper | May 24, 2021
Across industries, DevOps and DataOps have been widely adopted as methodologies to improve quality and reduce the time to market of software engineering and data engineering initiatives. With the rapid growth in machine learning (ML) systems, similar approaches need to be developed in the context of ML engineering, which handle the unique complexities of the practical applications of ML. This is the domain of MLOps. MLOps is a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably.
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