Understanding Edge AI

What is edge AI?

Edge AI, or AI at the edge, refers to the practice of integrating artificial intelligence systems—such as predictive analytics, speech or image recognition, or anomaly detection, with edge computing. It enables you to process data right where it was collected, typically on devices such as sensors, cameras, or other Internet of Things devices. Edge AI bypasses the need to send collected data back to remote servers in order to be processed. Processing data on the edge results in faster response times, reduced latency, and enhanced privacy and security. 

An example of edge AI would be a surveillance camera on a college campus that is equipped not only with processing capabilities, but also AI capabilities, such as facial recognition. This is beneficial because the camera could identify known bad actors nearly instantly, which is critical when seconds matter. Other good targets for quick edge processing and AI analysis would include autonomous vehicles and wearable health-monitoring devices. 

Edge AI is becoming increasingly popular thanks to several technological advancements, such as the advanced evolution of neural networks, more powerful computing infrastructure, and the widespread adoption of IoT devices. These advancements enable edge AI to deliver intelligent information and rapid on-the-spot insights to organizations across virtually all industries. With edge AI, you can automate processes and operations, improve safety, and optimize workflows. That information is more secure, too, because it never leaves the edge device and can’t be intercepted in transit to a central processing server.

Traditional vs edge vs distributed AI

Edge AI is different from traditional AI and distributed AI in several important ways. With traditional AI, the AI models are typically located at the backend on central servers, such as in a public cloud or data center, where they process and analyze data that is sent to the central location. This takes time and can also potentially cause server lag if too much data is being processed  and analyzed at the same time. With edge AI, each edge device can analyze the data it collects, using an integrated AI or machine learning model installed on the device. The device then makes intelligent decisions and transmits only the most critical insights and data to central servers or to the people who need to know, rather than sending all the raw data over. Edge AI is much more suited to time-sensitive processes and operations. 

Edge AI is also different from distributed AI. The concept of distributed AI lies somewhere between the concepts of edge AI and traditional AI. With a distributed system, data is processed and analyzed across a range of interconnected devices—including the central servers and edge devices. Each node in the interconnected chain works on a small portion of the analysis, with the idea that distributed processing can handle large workloads and can scale beyond simple edge devices. The downside to distributed AI is that it comes with more latency than edge AI, it’s less private and secure because data is transmitted across networks to various devices, and it’s more complex to manage.

Edge AI and cloud computing

Cloud computing is closely intertwined with edge AI. In fact, cloud computing enables edge AI systems by providing the hardware, applications, services, and tools needed to deploy, run, data sync, monitor, and manage AI models on the edge. 

When comparing cloud AI against edge AI, however, the following differences become clear:  

• Computational capabilities – Cloud AI has superior capabilities when it comes to general computing. However, although edge AI has a limit to how much processing power it can build into a device, it often has very specific parameters and doesn’t really require infinitely scalable compute power. 

• Storage capacity – The cloud, of course, offers a nearly unlimited amount of storage and can scale quickly and easily as needed. With edge AI, storage is limited on the edge device—but again, it typically has the amount it needs to process and analyze data and send insights back to HQ. 

• Latency – In this area, edge AI wins out over cloud AI. By processing data right there on location, latency stays ultra-low and response times are fast. Cloud AI systems must transmit data to various remote servers, which could be halfway around the globe, and latency is increased. 

• Network bandwidth – Edge AI requires very little bandwidth because the amount of data transmitted to central servers is so much less than with cloud AI. Where edge AI sends only the most critical insights and decisions to other servers, cloud AI sends all raw data to remote locations. That requires much higher bandwidth. 

• Security and privacy – By keeping data largely on the edge device, edge AI is inherently more secure and able to keep sensitive information private than cloud AI. Any data transmitted over networks to external servers, which is all the data in a cloud AI system, could be compromised or intercepted by malicious actors.

Advantages of edge AI for enterprises

Benefits of edge AI include: 

• Increased efficiency and agility – With faster response times and faster identification of potential issues, organizations can achieve more efficient and agile operations. 

• Reduced costs – Edge AI decreases the need for as many cloud resources, which keeps operational expenses down, and doesn’t consume as much bandwidth. Costs can be further reduced with less need for human monitoring, as most edge AI systems are largely autonomous. 

• Simpler compliance with security and privacy regulations – By keeping data on the device where it’s collected, organizations can more easily comply with data sovereignty regulations. Security and privacy are also enhanced by avoiding the need to transmit as much data across networks. 

• Enhanced energy conservation – Most edge AI devices consume very little power. Plus, power consumption of centralized servers and systems is also reduced due to the smaller processing load. 

• High availability – Because AI processing at the edge doesn’t require an internet connection, edge AI systems are often more available and reliable than traditional or cloud-based AI.

Edge AI use cases

AI is increasingly becoming a pervasive and critical tool across all industries and in a vast range of use cases. As edge AI matures, use cases for processing at the edge are also expanding. Some common and emerging use cases for edge AI include: 

• Healthcare – Medical devices such as AI-based blood glucose monitors and vital signs trackers can automate some decision making for doctors and relieve their workload. With on-device capabilities to alert healthcare teams if something goes wrong, patients can also go home from the hospital earlier without eliminating post-surgical monitoring. 

• Retail – Store shelves can be equipped with devices that monitor inventory and alert teams when products run low. Some retailers are also beginning to try out edge devices that track carts as customers shop and allow them to check out from anywhere in the store. 

• Smart homes and office buildings – Edge AI devices can monitor and control heating, cooling, lighting, and much more to optimize energy efficiency. Doorbell camera systems can also identify faces of visitors, detect motion, and alert the right people when anomalous behavior occurs. 

• Manufacturing – Edge AI devices on heavy equipment and machinery can perform predictive analytics to alert teams to potential failures. Cameras on production lines can identify defective products and automatically remove them from the line. Robots with edge AI devices can package products or assemble gear with a high level of adaptability to changing environments. 

• Smart cities – Edge AI devices are critical components in smart cities, where they identify free parking spaces downtime, for instance, and alert drivers in real time. Devices on traffic lights can analyze traffic patterns by time of day and adjust traffic signals to optimize traffic flow and avoid gridlock. 

• Transportation – Trucks hauling perishable or delicate items can be equipped with edge AI devices that monitor temperature, vibration, and other metrics and alert drivers and fleet managers when conditions are no longer optimal. 

• Utilities – Smart meters can monitor energy usage and alert home or business owners when consumption surpasses a preset limit. Pipelines and grid equipment located in very remote areas can also self-monitor and notify technicians when they need service or when potential failures might occur.

How Nutanix supports edge AI

Cloud computing is essential to edge AI, and Nutanix is committed to making the cloud simple and seamless for modern organizations. Nutanix Cloud Infrastructure (NCI) is an ideal foundation for any business that wants to future-proof its operations and make the most of advanced AI technology. 

With NCI, you can more efficiently monitor and manage workloads, data, and applications wherever they reside—including in the cloud and at the edge—with a single, unified management plane and toolset. 

Nutanix enables you to make the most of edge AI, delivering the high availability, security, low latency, speedy insights, cost savings, efficiency, and agility you need to stay competitive in an ever-changing market. We also empower you to keep pace with game-changing breakthroughs and innovations that could transform your operations and your bottom line.