Many of the edge computing principles that fueled the rapid rise of the Internet of Things (IoT) have made their way into the realm of AI and computer vision, enabling vision AI applications that can process data closer to the source.

Consolidating computer vision development at the network edge has the potential to reduce the number of roadblocks in the way of many production-ready vision AI applications. This can be impactful across industries and markets, streamlining the tools, stakeholders and investment needed to get AI models off the ground. 

In this post, we’ll dig into the converging concepts of edge computing, IoT, machine learning (ML), AI, and computer vision, and how they’re working in concert to unlock the long-held promise of digital transformation across markets.

What is Edge AI?

Edge AI involves integrating AI applications that are capable of processing data within or near the physical devices where that data is collected. Rather than sending data to a central repository in the cloud for processing, for instance, edge AI solutions can handle all computation “on site,” so to speak. 

The benefits of creating solutions that shorten data’s journey from collection all the way to operationalization truly run the gamut. With fewer “hops” between the data source and the application processing that information, businesses in virtually any sector can deploy ever-nimbler computer vision solutions to improve their operations. 

As well as enabling real-time speeds and more efficient analysis, there is a security and privacy component in leveraging edge AI. With many companies foregoing the risk of data exposure in public or hybrid cloud environments, for instance, having the ability to collect and analyze data closer to the source is often a business imperative. 

Why is Vision AI at the Edge Important?

With respect to computer vision, edge AI solutions enable the development of more automated processes that can derive actionable insights in near-real time. Since speed and agility sit at the core of many vision AI applications (ie. monitoring security risks, ensuring compliance), edge AI can make the operationalization of these solutions less complex—and even more accurate—as there are fewer tools and data handoffs involved. 

A few different (and recent) innovations are largely responsible for the rise of edge AI. Neural networks—the infrastructures enabling machine learning within AI—for instance, have matured and advanced significantly in the recent past as businesses across industries began putting their wealth of collected data to work. Similarly, GPUs have become more powerful and distributed, enabling smarter, more capable devices that can handle end-to-end AI computation at the network edge. 

Perhaps most important, however, has been the increasing ubiquity of IoT broadly, and the generalized acceptance of more devices, in more locations, collecting more data than ever before. The fact that businesses and the general public have become increasingly comfortable with IoT has enabled data collection at the scale needed to ultimately make edge AI a useful reality. 

This convergence of technological advancement and a need for greater business agility has enabled vision AI applications to be effective across a wider breadth of business applications and in the face of specific challenges (ie. security and compliance) that in the past may have been daunting. This includes when there are low bandwidth connections—if not no connectivity whatsoever—between the edge and the cloud, or even when high-volume data collection models make cloud computing cost prohibitive. Even compute-intensive models sometimes require closer proximity to data collection to perform successfully, while other situations benefit from the efficiency of real-time responses or alerting—whether for privacy, safety or security—that only edge deployment can ensure.

3 Real-World Examples of Edge AI

While not all Edge AI applications involve computer vision, many of the most successful and impactful ones do. Here are just a few:

  • Manufacturing: Many factories have long deployed CCTV or security cameras across the factory floor. These devices can start doing double duty, automatically monitoring operations while actively tracking for real-time hazards, acting as the source for alert automation and enabling businesses to create visual data records for traceability and regulatory compliance. 
  • Healthcare: When lives are potentially at stake, AI-powered tools that can deliver immediate insights are critical. Even for asset tracking, edge AI powered by computer vision can provide immediate alerts with almost no latency since data doesn’t need to travel far to be processed, helping get the right equipment to the right patient quickly.
  • Retail: Style is subjective, and the beauty of AI is that it can learn to recognize shopper interest and demand via visual cues gleaned from edge devices. Whether deployed as a virtual shopping assistant or even as a tool for gauging attitudes toward certain displays or products, smart cameras on the retail floor can help retailers adjust their business in real-time to maintain a competitive edge.

 For any of these scenarios to succeed, edge AI devices need to be powered by platforms that make the computer vision lifecycle manageable, whether teams are versed in the technical aspects of machine learning or not. With Plainsight, customers can integrate edge vision AI into their businesses to solve daily and industry challenges. Plainsight facilitates deployment, monitoring, and ongoing management of vision AI solutions from start to finish. 

 With an all-in-one platform that delivers complete control over the entire computer vision lifecycle, teams don’t have to wait to explore Vision AI, or even make significant upfront investments in new technology to see its value. Schedule a demo today to learn how Plainsight can help streamline the computer vision life cycle across any business’ value chain.

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