Tag: AI

Extreme Networks Showcases AI-Based Innovation

This week Extreme Networks held its Extreme Connect event in Nashville. This is the company’s annual user conference, highlighting the latest and greatest in the company’s efforts to innovate.

Extreme has been on an interesting, multi-year journey as the company has grown through aggressive acquisitions. Much of its research and development efforts have focused on bringing the portfolios of core Extreme together with the businesses it has purchased, such as Avaya Networking, Brocade Data Center, Motorola Wi-Fi and Aerohive.

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Extreme Networks Showcases AI-Based Innovation

How AI-based digital twins accelerate point-of-sale (POS) testing

Arguably, no other industry has been impacted by digital transformation (DT) as much as retail. Retail has undergone a major transition over the past few years, with customer experience (CX) becoming the No. 1 brand differentiator.

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How AI-based digital twins accelerate point-of-sale (POS) testing

GTC 2022: Nvidia flexes its GPU and platform muscles

Flamboyant CEO Jensen Huang’s 1 hour, 39-minute keynote covered a lot of ground, but the unifying themes to the majority of the two dozen announcements were GPU-centered and Nvidia’s platform approach to everything it builds.

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GTC 2022: Nvidia flexes its GPU and platform muscles

AI critical to business success in post-pandemic world, study finds

Enterprise data cloud provider Cloudera today released a new study, “Limitless: The Positive Power of AI,” which explores how organizations use artificial intelligence (AI), machine learning (ML) and data analytics to improve business outcomes in a post-pandemic world. The study also examines the rise of environmental, social and corporate governance (ESG) and how organizations choose to use these technologies for the greater good.

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AI critical to business success in post-pandemic world, study finds

AIOps lessons learned: Be careful when selecting a vendor

The concept of AIops is simple: Infuse artificial intelligence(AI) into IT to make operations speedier and more efficient. In theory, AIops at its best should lead to an autonomous IT environment in which functions can run themselves with little or no human intervention. In practicality, the path to this nirvana state is anything but straightforward and raises several questions. Where should you start? How do you measure the value? Is AI ready to scale across production environments? Do I need new tools?

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AIOps lessons learned: Be careful when selecting a vendor