Edge AI in Practice & Some Real-Life 

Edge AI in Practice & Some Real-Life 

As time advances, the usage of IoT (internet of things) applications and mobile computing is rising. Today, billions of IoT and mobile devices are connected to the internet, producing colossal amounts of information at the network’s edge. This results in a massive collection of data volumes in cloud centres incurring extreme network bandwidth and high latency usage. 

It is because of such a reason pushing the AI frontier to the network edge has become a dire need at the moment to unlock big data’s full potential. The amalgamation of AI and edge computing is called edge AI, which is the primary concept for avante-garde AI applications. 

Also read: “The Paradigm shift from Edge to Edge AI. What are the drivers bringing this change?

To better understand edge AI’s real-life application practices, it is vital to understand the concept in-depth.

What is Edge AI?

The mingling of Artificial Intelligence (AI) and Edge Computing is accurately called Edge AI. It runs ML (machine learning) tasks straight on edge-connected devices. To get a better insight into edge AI, understanding a few technological trends which drive the need to shift to edge from AI computing is essential. 

Interesting Read – The paradigm shift from Edge To Edge AI: What are the drivers bringing this change

Edge AI is steered by IoT and Big data.

In recent times of IoT, unprecedented data volumes are generated through connected devices required to be gathered and analysed. As a result, huge quantities of real-time data are generated that need AI systems to perceive the data. 

AI is traditionally cloud-based

In the beginning, all the AI solutions were driven by the cloud as the requirement for high-end hardware that can conduct deep-learning computing tasks and scale cloud resources effortlessly. Such needs data offloading to external (cloud) computing systems for future processing. However, this aggravates latency, enhances communication expenses, and leads to privacy concerns. 

Edge Devices

Edge device is either an edge server or an end-device that can conduct computing work on your device. An edge device processes the information of connected sensors which collect data, for instance, cameras which offer video streams. Edge devices are servers or computers of a broad platform range. Any computer of every form factor can act as an edge device, from mobile phones and laptops to embedded computers, physical servers or personal computers. 

Market trends related to Edge AI

Whenever a new technology is introduced, a lot of buzz goes around it. However, the increasing demand for the Edge AI market has concrete reasons. As per the data of  “Global Edge AI software market growth”, the software market of edge AI alone will flourish from $346.5m to $1.1Bn by FY 2024. Edge AI consulting and hardware market will enhance at a similar pace. It has been estimated that the entire global edge computing market will increase by 37.4% and amount to $43.4Bn by FY 2027. 

Edge AI and 5G 

5G network construction is initiated gradually; they are initially set up locally in areas with dense populations. With the assistance of 5G networks, fast and large collection streams are enabled. Technology like edge Ai expands efficient analysis and utilisation of such data streams. 

Customer experience

Contemporary people expect seamless and smooth service experiences. In customer services, a delay of seconds can be costly when dealing with consumer experience. Edge computing helps respond to this requirement by eradicating delays originating from data transfer. 

GPU processors, cameras, sensors, and other hardware are becoming increasingly cheap; therefore, customised and exceedingly customised solutions of edge AI are becoming accessible to many people. 

Huge amounts of data generated by IoT

Sensor technology and IoT manufacture large quantities of data, which can be tricky and impossible in actual practice. The application of edge AI makes it possible by entirely utilising hyped IoT data. Huge quantities of sensor information are analysed locally, and operations decisions are automated. It is only the most crucial information stored in an information warehouse in a data centre or the cloud. 

Real-time practices of Edge AI in different industries

Intelligent transportations: With edge AI, drivers can gather and share data from traffic data centres. This helps vehicles in avoiding danger as well as stop abruptly. Such is implied in real-time, helping to avoid accidents. Edge AI also assists crewless cars in sensing their surroundings and safely moving. 

Retail: Huge retail chains have been conducting customer analytics for a long time. These analytics are based on complete purchase analysis, that is, receipt data. Even though satisfactory results can be attained through this method, it doesn’t say the number of people strolling around the outlet, when they paused to see something, or how satisfied they are. Video analytics that analyse entirely anonymised information are extracted from video images, offering an insight into an individual’s buying behaviour, enhancing consumer services and the entire shopping experience. Edge AI is practised to get a better result for the same.

Manufacturing: Edge AI is incessantly used in the manufacturing sector for quality control. Latest machine vision like edge AI monitors product quality uninterruptedly, with precision. Edge Ai, which is video-assisted, detects the tiniest quality deviations often missed by the human eye. 

Smart Factories: In smart factories, edge AI is implemented in smart machines, aiming for better productivity and safety. For example, operators can use the remote platform to operate heavy machines, especially those situated in comfortable and safe places in unsafe and hard-to-reach places. 

Smart Energy: Using a Smart Grid for monitoring and providing power can benefit in various ways. The vast data produced by using these smart grids based on edge AI can help us determine the consumption, forecasting and monitoring. As opposed to cloud-based services, edge AI removes the communication lag between multiple devices.

AI Healthcare: Using Edge AI proves to be a boon for remote diagnostics and surgery. It also helps monitor patients’ vital signs depending on edge devices which conduct AI close to the edge. Using a remote platform, doctors can operate surgical instruments from a distance where they feel comfortable and safe. 

Entertainment: Edge AI is implemented for entertainment purposes, for instance, in virtual reality, mixed reality and augmented reality like in VR (virtual reality) glasses to video streaming content. The sizes of VR glasses are regulated through offloading computation to edge servers from the VR glasses close to end devices. 

Smart AI vision: Edge AI  is implemented in vision computer applications like live video analytics to energise AI vision devices in different industries. “Visual Processing Units” can power heavy-performance vision computer applications on edge devices.  

Also Read: 6 Most Innovative Implementations Of Edge AI

While companies today are making tech investments as part of their digital transformation journey, other forward-looking organizations and cloud companies see Edge AI as a new and open opportunity to innovate further.

With applications of Edge AI wide open, companies today have already implemented it in various industries like Consumer Market, Industrial, Automobiles, etc. But with further advancements in the years to come, it’ll play a crucial role in facial recognition, Industrial IoT, Emergency Medical Care and others. With some more time on the calendar, Edge AI will drive our everyday devices.

ENAP Studio  – Decentralizing AI to deliver AI on Edge

ENAP Studio is an Edge AI platform that helps developers leverage AI capabilities on edge devices and simplify their transformation journey. EDGENeural.ai works with the vision to decentralize AI. It provides a platform that enables developers a one-stop solution and helps companies cut down the costs and time required to go to market.

ENAP Studio by EDGENeural.ai is an end-to-end workflow solution, focused on improving the efficacy of AI algorithms and models for Edge devices. It has a fully-integrated modular workflow that allows developers to easily Train, Optimize and Deploy Edge AI Neural Networks.

Sign up for free to get started: https://edgeneural.ai/beta-access/ 

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