Using AI and ML to Extract Actionable Insights in Edge Applications - RTInsights

If data starts at the Edge, why can't we do as much as possible right there from an AI point of view? The explosive growth in Edge devices and applications requires new thinking as to where and how data is analyzed, and insights are derived. New Edge computing options, coupled with more demanding speed-to-insight requirements in many use cases, are driving up the use of artificial intelligence (AI) and machine learning (ML) in Edge applications. Where AI and ML are applied (at the Edge or in a data center or cloud facility) is a complex matter. To get some insights into current strategies and best practices, we recently sat down with Said Tabet, Chief Architect, AI/ML & Edge; and Calvin Smith, CTO, Emerging Technology Solutions; both in the Office of the Global CTO at Dell Technologies. We discussed the growing need for AI and ML to bring sense to the large amount of Edge data that is generated today, the compute requirements for AI/ML in Edge applications, and whether such computations should be done at the Edge or in a data center or cloud facility. RTInsights: What are today's emerging trends, and how do AI and ML fit into the Edge discussion?