What is edge computing?

In its simplest terms, edge computing is a cloud-based solution that is augmented by the deployment of a specialized computing device “close to the action”. This helps save bandwidth and increase performance, often for resource-intensive applications related to AI and machine learning. An example is the Neal Analytics StockView application, where an Azure Stack Edge device handles the visual AI components that detects stock-outs and provides alerts, and the cloud is used for data storage, BI, and training AI models.

TL;DR

Edge computing can enable a variety of use cases and business scenarios that can revolutionize your business by deploying AI and machine learning resources “right up close.

I’ve always thought a gaming console was a good analogy for edge computing. Think about it: most games, even if not explicitly online, are supported by at least two cloud services:

The console manufacturer’s cloud service that enables game purchases, tracking trophies, backing up saves and when applicable facilitates the connection to the game publisher’s online gaming service.
The cloud service for the actual game that enables online play, tracks in game collectibles, world states, customized characters, and the like.

While games “can” stream with varying degrees of success, one of the benefits to using a console is that the complex and data intensive computations can happen locally before the relevant data for online gaming is sent over the internet. When playing games that require quick reactions, the time efficiency of a console can be the difference between winning and losing.

Edge computing works in much the same way: AI and machine learning workloads are deployed at “the edge”, right up close to the relevant business process, customer transaction and the like, saving you precious seconds, removing connectivity issues from the equation, and ensuring greater performance overall.

Key benefits of edge computing

While cloud computing IoT devices mostly rely on internet speed, connectivity, and stability, the edge-based IoT devices can run as automotive units, reducing potential risks and vulnerabilities in internet connections and cloud software. Edge computing also helps reduce many complexities related to bandwidth and network latency.

Here, we discuss a few key benefits of edge computing:

Speed and latency: When all the IoT devices share a central server, managing large volumes of data may create complexities within the server and slow down the IoT system. But edge computing allows data processing at each device, thus eliminating the need for data to travel to a centralized server, making the processing faster. As the data movement requirement is reduced, the response time also gets reduced, all of which contributes to higher efficiency.
Security: A single DDoS attack can entirely shut down the operations of a business. Particularly, a central server is more prone to security breaches which can shatter the whole system. But, if security systems are available at each fragment of the system, glitches can be solved separately, without disturbing the other parts of a system. Edge computing allows additional security by implementing firewalls and security scans while the data is exchanged between the internet, servers, and nodes.
Scalability: Edge computing offers an advantage of scalability to the network. An IoT system generates a lot of data which is processed by a central center making the system slow and may even cause breakdown. On the other hand, edge computing can work simultaneously at different fragments with efficiency, reducing traffic and processing burden on the central data repository.
Cost saving: Not all data is important and worth spending the same amount of money on transporting, managing, and securing. Edge computing collectively identifies and categorizes data that is relevant for the organization, helping reduce redundant costs on data storage. Edge computing allows interoperability between modern devices and legacy devices by converting the communication protocols that smart devices and the cloud understand. So, the older devices can be used without the need for new expensive equipment.
Regulatory compliance: With edge computing, it becomes easy to filter sensitive information locally and only process non-sensitive or important data model building information to the cloud. This helps an organization build an adequate security and compliance framework.

Edge computing use cases

Business scenarios that can benefit from speed and performance aspects of edge computing include the following:

Edge computing use cases

Manufacturing safety: In situations where machines need to be shut off as fast as possible to prevent injury or damage, the performance gains from edge computing can be the difference between someone getting seriously injured or just having a close call.
Retail stock management: Solutions like StockView from Neal Analytics can provide retailers with real-time tracking of products on shelves, which can be extremely useful for retailers providing online ordering and in-person pickup. Deploying the visual AI workload at the edge will give you faster detections and a better customer experience for people who order online, pick-up in person, and for customers who might leave if the item they want isn’t in stock.
People counting: Neal Analytics has built solutions that will count the number of people entering and exiting your retail space, which can be a critical part of how your company manages safety regulations during the Covid-19 pandemic. Deploying this type of visual AI workload at the edge ensures you have the most up to date information and can keep both your employees and your customers safe.
Medical: Using an edge device to develop and operationalize medical AI solutions (visual ones especially) can both protect patient data and reduce the strain on internet bandwidth that is needed for other mission critical applications.
Security: Whether it’s a potential intruder or just someone wandering into a dangerous area, seconds count when it comes to detecting that someone is where they shouldn’t be. The edge can be a critical part of security and safety solutions that can provide rapid alerts of potential intrusions or safety violations.

In short: Edge computing can enable a variety of use cases and business scenarios that can revolutionize your business by deploying AI and machine learning resources “right up close”. Neal Analytics has co-developed a variety of edge solutions with many of the world’s leading technology companies for retail, medical, and industrial applications, and can help you figure out how your business can best leverage the combination of AI, machine learning, and the edge.

Contact our team to learn how your business can leverage edge computing.