Edge AI – The Next Stage of Digital Transformation?

In the last few years, artificial intelligence implementations in various companies have changed around the world. As more enterprise-wide efforts dominate IT, Cloud Computing has become an essential component of AI’s evolution. Now as customers spend more time than ever on their devices, businesses increasingly realize the need to bring essential computation onto the device to serve its customers. This is the reason that the edge market will only accelerate in growth over the next several years. According to IDC’s Worldwide Spending Guide, edge computing spend will reach $250 billion in 2024.

To get out on front, large cloud companies have begun offering edge computing services. Intel and Udacity have partnered up to offer free training courses on Edge AI for IoT developers. While most data is currently processed in large data center or cloud environments, more and more of the compute load will be pushed to the edge or even onto the devices themselves. It’s clear that industry leaders see edge as the next paradigm of enterprise technology, but what is it and what is pushing it there?

Driving to the Edge and Beyond

Edge computing is a distributed computing paradigm that brings computation and storage closer to the device itself rather than increasing density within a data center. This architecture can trace itself back to the content delivery network (CDN) of the late 90’s that pushed popular web content to multiple parts of the world wide web in order to better service users with lower latency and stress on the broader network.

But edge is not the 4^th wave of technological transformation that will replace that cloud, just as the client/server relationship replaced the mainframe terminal. Edge computing must be thought of in partnership with the cloud, just as two tools in the technology toolbox for enterprises. On one end, big data and analytics operating at scale that is essential for solving the biggest problems. The other, instant feedback that is generated for the user and can be used to deliver more optimal and personalized experiences or outcomes.

So what is driving data and computing to the edge?

• Latency – Even with dedicated pipelines and optimal conditions, sending data over the network accrues latency. In this, there is no substitute for proximity.
• Privacy and Security – As the demand for personalized experiences grows, so too does the sensitive nature of the data being collected. Not only does the edge paradigm add an layer of segmentation, but the right security policy and encryption can be personalized for the nature of the data and the device.
• Load balancing and resiliency – The more end points available to distribute the load an application, the more you can balance that load across them in addition to providing greater levels of resiliency and redundancy should segments of the network go down.

 This is but an abbreviated list of the benefits of deploying AI to the edge, but it should give you a taste of the benefits it offers.

What comes next

The edge is a complex piece of IT architecture and will require developers, network operators, security specialists, and data scientists to crack the code. But the lessons learned in the past decade from the cloud revolution can be reapplied to this next wave of innovation at the edge, as new use cases will be tried and fail and some may change the world. What could it mean for your business to be able to provide immediate personalized interactions with your customers, systems, supply chain, patients, or even your own employees? Deploying AI at the edge gives enterprises the ability to act on this data faster and in ways not possible before.  

To Learn More

Dell Technologies can help you realize the full potential of your data and opportunities at your edge. To learn more about how our services and offerings can help you accelerate your initiatives at the edge, or to explore how to make your edge work for you, check out solutions from Dell Technologies and Intel.