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Which workloads are best suited for cloud vs. on-premises or edge?

CIO Business Intelligence

Enterprises driving toward data-first modernization need to determine the optimal multicloud strategy, starting with which applications and data are best suited to migrate to cloud and what should remain in the core and at the edge. A hybrid approach is clearly established as the optimal operating model of choice.

Cloud 257
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The early returns on gen AI for software development

CIO Business Intelligence

But early returns indicate the technology can provide benefits for the process of creating and enhancing applications, with caveats. The key to success in the software development lifecycle is the quality assurance (QA) and verification process, Ramakrishnan says.

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Going ‘AI native’ with in-house ChatGPT the MITRE way

CIO Business Intelligence

Most recently, MITRE’s investment in an Nvidia DGX SuperPod in Virginia will accelerate its research into climate science, healthcare, and cybersecurity. The AI data center pod will also be used to power MITRE’s federal AI sandbox and testbed experimentation with AI-enabled applications and large language models (LLMs).

Nonprofit 335
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Which Workloads Belong On-Premises as Part of Hybrid IT

CIO Business Intelligence

Enterprises driving toward data-first modernization need to determine the optimal multicloud strategy, starting with which applications and data are best suited to migrate to cloud and what should remain in the core and at the edge. A hybrid approach is clearly established as the optimal operating model of choice. Close to the Edge.

Cloud 246
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What is an automation engineer? A growing role to address IT automation

CIO Business Intelligence

The automation engineer role Automation has been a cornerstone of the manufacturing industry for decades, but it’s relatively new to the business, healthcare, and finance industries. Run tests for databases, systems, networks, applications, hardware, and software. Install applications and databases relevant to automation.

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Data labeling

Dataconomy

Data labeling is a critical process that lays the groundwork for effective machine learning applications. Without accurately labeled data, the effectiveness of AI applications diminishes significantly, making this process an indispensable component of successful machine learning projects.

Data 36
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CIOs set their agendas to achieve IT’s ultimate balancing act

CIO Business Intelligence

Operational excellence for Mate means ensuring a new employee has the right equipment and applications on day one and every day thereafter. Systems should never go out too early just to meet a timeframe, Taylor stresses, noting that quality is better than speed. I have to close the gaps,” he says.

Hotels 342