Everything about Edge AI
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Edge AI at its Simplest is a Combination of Edge Computing & AI.
But Let’s Start Simple.
What is Edge Computing?
Edge computing is a distributed computing architecture in which customer data is processed on the edge of the network, as close as possible to the source of origin.
It is a distributed computing framework that brings together enterprise applications with data sources such as IoT devices or local border servers. This proximity to the data source can offer strong commercial advantages: faster information, improved response times, and better bandwidth availability.
This is computing conducted at the user’s physical location or near the data source.
It is made up of multiple techniques which bring data collection, analysis, and processing to the edge of the network. This means that computing power and data storage are located at the location of data collection.
You can think of Edge AI as analytics that takes place locally and utilizes advanced analytics methods (such as ML & AI), edge computing techniques (such as machine vision, video analytics, and sensor fusion), and requires suitable hardware and electronics (which enable edge computing). Furthermore, intelligence geolocation methods are often needed to make Edge AI happen. -Advian
Pros of Edge Computing.
Let’s discuss the Top 6 Benefits.
Latency & Speed
The longer it takes to process data, the less relevant it is. In the case of the autonomous vehicle, time is of the essence and most of the data it collects and requires is useless after a couple of seconds. Milliseconds matter, especially on a busy roadway. Milliseconds also matter in the digital factory where intelligence-based systems perpetually monitor all aspects of the manufacturing process to ensure data consistency. In many cases, there isn’t time to round trip data back and forth between the cloud. Situations such as equipment failures and hazardous incidents call for the instantaneous analysis of data. Confining data analysis to the edge where it is created eliminates latency, which translates into faster response times. This makes your data more relevant, useful, and actionable. Edge computing also reduces the overall traffic loads of your enterprise at large, which improves performance for all your enterprise applications and services.
Cost-Efficient
Since all data is not the same and does not contain the same value, how does one justify spending the same amount of money on all of it when it comes to transporting, managing, and securing it? While some data is critical to your operations, some are expendable. Edge Computing allows you to categorize your data from a management perspective. By retaining as much data within your edge locations, you reduce the need for costly bandwidth to connect all your locations, and bandwidth translates directly into dollars. Edge computing isn’t about eliminating the need for the cloud, it is about optimizing the flow of your data to maximize your operating costs. Edge computing also helps to reduce some level of data redundancy. Data that is created at the edge must be stored there at least temporarily. When sent to the cloud, it must be stored again, creating levels of redundancy. When you reduce redundant storage, you reduce redundant cost.
Higher Scalability
Although the idea that edge computing offers an advantage of scalability may seem contrary to promoted theory, it makes sense. Even for cloud computing architectures, data must first be routed to a centralized data center in most cases. Expanding or even changing dedicated data centers is an expensive proposition. What’s more, IoT devices can be deployed along with their processing and data management tools at the edge in single implantation, rather than waiting on the coordination of efforts from personnel located at multiple sites.
Improved Security
When all your data must eventually feed to its cloud analyzer through a single pipe, the critical business and operating processes that rely on actionable data are highly vulnerable. As a result, a single DDoS attack can disrupt entire operations for a multinational company. When you distribute your data analysis tools across the enterprises, you distribute the risk as well. While it can be argued that edge computing expands the potential attack surface for would-be hackers, it also diminishes the impact on the organization. Another inherent truth is that when you transfer less data, there is less data that can be intercepted. The proliferation of mobile computing has made enterprises much more vulnerable because company devices are now transported outside of the protected firewall perimeter of the enterprise. When data is analyzed locally, it remains protected by the security blanket of the on-premises enterprise. Edge computing also helps companies overcome the issues of local compliance and privacy regulations as well as the issue of data sovereignty.
Increased Reliability
The world of IoT includes some pretty remote territories comprised of rural and less than optimal environments concerning internet connectivity. When edge devices can locally store and process ensuing data, it improves reliability. Prefabricated micro data centers are built today to operate within about any environment. This means that temporary disruptions in intermittent connectivity will not impact smart device operations just because they lost connection to the cloud. In addition, every site has some built-in limitation to the amount of data that can be transmitted at one time. Although your bandwidth demands may not be evaluated yet, the exponential growth in generated data will push bandwidth infrastructure to the limit in the future for many enterprises.
Real-Time Analytics
With Edge Computing, it is possible to reach near real-time analytics. The analysis takes place in a fraction of a second — which is crucial in time-critical situations. Let’s think about machines on a factory assembly line. If a robot on the assembly line is activated at the wrong time or too late, it may result in a damaged product, or the product may move further on the assembly line unprocessed and untouched. If the mistake goes unnoticed, the faulty product may end up in the market or cause damage in later phases of the production.
Cons of Edge Computing
Difficulty in Preventing Security Breaches
Edge devices are more difficult to pinpoint at an enterprise level as well, making it difficult to monitor localized devices that work with enterprise data and determine if they are following the enterprise network’s security policy. For organizations working to implement a zero-trust approach to network security, devices with limited authentication features and visibility on the network can pose a challenge to overall network security.
Geographic Disparities
Although edge computing enables more opportunities for data processing and storage at a localized level, some geographic regions may be at a disadvantage when it comes to edge implementation.
In areas with fewer people and financial or technical resources, there will be fewer active edge devices and local servers on the network. Many of these same areas will also have fewer skilled IT professionals who can launch and manage a local edge network’s device.
(For Understanding what AI Is, Click Here)
How Does Edge AI Work? (Source: Advian)
In a typical machine learning setting, we first start by training a model for a specific task on a suitable dataset. Training the model means that it is programmed to find patterns in the training dataset and then evaluated on a test dataset to validate its performance on other unseen datasets, which should have equivalent properties to the ones that the model is trained on.
Once the model is trained, it is deployed or “put to production”, meaning that it can be used for inference in a specific context, for example as a microservice. The model works via an API, and its endpoint(s) are queried for predictions by pinging them with input data. The model output is then either communicated to another software component or in some cases displayed on the application frontend.
When a trip is requested, the Uber app displays the customer’s scheduled pickup time on the UI.
Market Growth in Edge AI
According to the Global Edge AI Software Market Growth Report, the Edge AI software market alone will grow from $ 346.5 million to about $ 1.1 billion by 2024. Edge AI hardware and the consulting market will grow at the same pace. Grand View Research estimates that the total global Edge computing market will grow 37.4 percent per year and will be worth $ 43.4 billion by 2027.
Trends in Edge AI
5G
5G networks enable the collection of large and fast data streams. The construction of 5G networks begins gradually, and initially, they will be set up very locally and in densely populated areas.
The value of Edge AI technology increases when the utilization and analysis of these data streams are done as close as possible to devices connected to the 5G network.
IoT Generated Data
IoT and sensor technology produce such substantial amounts of data that even collecting the data is often tricky and sometimes even impossible in practice.
Ex: The latest Airbus A350 Air Crafts has 50,000 sensors that collect 2.5 Terabytes of data every day. In comparison, this is more data than the whole Wal-Mart’s massive Teradata data Warehouse had in 1992.
Customer Experience
People expect a smooth and seamless experience from services. Nowadays, a delay of just a few seconds could easily ruin the customer experience. Edge computing responds to this need by eliminating the delay caused by data transfer.
In addition, sensors, cameras, GPU processors, and other hardware are constantly becoming cheaper, so both customized and highly productized Edge AI solutions are becoming available to an increased number of people.
Various Use Cases and Examples of Edge AI
Retail
Large retail chains have been doing customer analytics for a long time. The analytics is currently largely based on an analysis of completed purchases, i.e. receipt data.
Although satisfactory results can be achieved with this method, the receipt data does not tell you everything. It doesn’t tell you how people move around the store, how happy they are, what they stop to watch, etc.
Video analytics analyses fully anonymized data extracted from a video image and provides an understanding of people’s purchasing behavior that can improve customer service and the overall shopping experience.
Energy
A smart grid produces a huge amount of data. A truly smart grid enables demand elasticity, consumption monitoring and forecasting, renewable energy utilization, and decentralized energy production.
However, a smart grid requires communication between devices, and therefore transferring data through a traditional cloud service might not be the best alternative.
Transportation
Passenger air crafts have been highly automated for a long time. Real-time analysis of data collected from sensors can further improve flight safety.
While fully autonomous and fully unmanned ships may not become a reality until years from now, modern ships already have a lot of advanced data analytics.
Edge AI technology can also be used, for example, to calculate passenger numbers and to locate fast vehicles with extreme accuracy. In train traffic, more accurate positioning is the first step and a prerequisite towards autonomous rail traffic.
Thanks for Reading, Happy Learning!
