Edge AI: Combining Edge Computing and Artificial Intelligence

In a world where the presence of AI is growing at an accelerated rate, the fusion of its capabilities with edge computing cannot be ignored. As AI continues to revolutionize industries, the flexibility of edge computing becomes increasingly vital. Against this backdrop, a profound technological revolution has unfolded, fueling heightened technology-driven demands in businesses. The unprecedented daily data creation has spurred innovative approaches to data management, storage, and processing. This surge in data has necessitated novel solutions, including the dynamic interplay between AI, edge computing, and creative data utilization across various sectors.

The report by Grand View Research states that the global Edge AI market is projected to grow at a compound annual Growth Rate (CAGR) of 21.0% by 2030.

These numbers signal the increasing trend of Edge AI in the market, making it difficult to overlook the potential of AI, edge computing, and the proliferation of IoT devices that together can help unlock the power of Edge AI.

This amalgamation indeed has opened new horizons for Edge AI that previously seemed impossible. Right from having driverless cars on freeways it has the potential to deliver secure banking, smarter content, and ad services, better healthcare, and the list of Edge AI continues.

In fact, in the digital era, almost every business contains job functions that can immensely benefit from adopting Edge AI. Now that we have a fair idea about this booming technology, let’s take a deeper look at it in this blog.

What is Edge Computing and Artificial Intelligence

In simple terms, Edge Computing means to bring computing power close to the source of the collected data. It does not rely completely on a centralized cloud infrastructure. Edge Computing processes the data close to its point of generation; at the edge of the network. Hence, it is called Edge Computing.

Edge Computing has better processing speeds even with greater volumes of data. It can generate quicker results which helps in real-time analysis of the data and can be efficiently integrated into different business organizations. Additionally, it provides businesses with the flexibility to utilize, enhance, and manage their physical assets in various ways, bringing the digital world into the physical.

On the other hand, AI stimulates human behavior and combines it with machines for a better output. And since AI and Machine Learning (ML) go hand in hand, for AL/ML services to work, there needs to be a good infrastructure and hardware in place. AI services run smoothly only when the right algorithms, software, and hardware are in place.

AI systems also analyze data to make predictions and detect patterns which will help with the learning process. AI has proven itself to be useful in doing several different tasks in various businesses. Some advantages of AI services are that it is less prone to errors, repetitive work is done quicker, and detail-oriented tasks are performed well, leading to many new and interesting developments.

Edge AI: Combining Edge Computing and Artificial Intelligence

Edge AI architecture

Fig 1. Edge AI Architecture (Image Source)

The technologies Edge Computing and Artificial Intelligence over time have demonstrated how they can help with and significantly impact diverse functions for businesses. Now, imagine how beneficial it will be to combine these two technologies. Edge AI blends Edge Computing and Artificial Intelligence into one technology capable of carrying out tasks in both fields smoothly and efficiently. Edge AI runs AI algorithms locally on a hardware device using the data that is collected through Edge Computing.

Edge AI gained further traction with an elevation in 5G services all over the world. This technology is flexible in enabling support for smart devices in different industries. Edge computing complements AI applications. It allows AI to work with devices and users at the edge, addressing overall latency and efficiency issues. With Edge AI, data is gathered in real-time, which reduces power consumption as well as cost because it doesn’t require an internet connection all the time. In fact, Edge AI can be a critical element of 5G in terms of high-speed and low latency benefits.

Edge Computing also benefits from its association with AI. The way AI algorithms work paves the way for Edge Computing to become optimized for its functioning. AI effectively mines data that suffers from overwhelming processes in Edge Computing. This removes the optimization problem with data that Edge Computing faces. Edge AI is an effective and mutually beneficial solution for Edge Computing and Artificial Intelligence.

Advantages of Edge AI

Edge AI has been widely used in the last couple of years. Edge AI is used without necessarily realizing its benefits. Google maps pushing alarms related to bad traffic, smartphones (iPhone) using facial recognition to unlock the device, emergency brakes in autonomous cars using AI algorithms to predict possible collisions, etc., are a few examples of Edge AI currently being used.

Edge AI can be an advantageous addition to businesses and their infrastructure.  Let’s take a closer look at them:

Flexibility

Edge AI enables smart devices to provide support for industries in different capacities because it is flexible. Traditional AI models depend on central servers. But Edge AI works on edge servers allowing businesses to specifically tailor operational requirements. This helps ease functionality for seamless processing and meet the customs needs of organizations.

Saving Energy and Bandwidth

Cloud data centers require a lot of power because of constant back and forth between the user server and the cloud. This requires extra time and slows down the data transfer process. Edge AI eases the burden of power bills for businesses. It doesn’t need an internet connection all the time while maintaining good speed and reducing data transfer load on networks.

Reduced Latency

 Cloud platforms are burdened with data. Edge AI helps to reduce the load of cloud networks; ultimately reducing latency. Data being analyzed locally by Edge AI improves the data transfer rate between the cloud and the businesses.

Real-Time Analytics 

Devices that integrate Edge AI, especially smartphones and IoT devices provide data in real-time. With Edge AI, the need to send the data back and forth between the source and centralized servers is eliminated. When the data is processed locally it gives instant responses and insights giving a huge boost to real-time analysis tasks.

Security and Privacy

With the increase in cyberattacks, security and privacy have become of paramount importance to businesses. This is where Edge AI proves to be extremely beneficial as it conducts processing at the edge of the network and closer to the source. It adds an extra layer of security for sensitive data. Edge AI helps enhance data protection and security, making it difficult to be attacked.

Edge AI comes with a plethora of advantages that will have a positive impact on businesses but comes with a set of limitations.  Edge AI requires a proper infrastructure with specific software and hardware which is not common for all organizations. It is a little behind in computing power when compared to cloud systems. Dependency on edge devices makes way for a variety of machines that can invariably lead to frequent failures. Nevertheless, there is always room for improvement especially when it comes to technologies.

In a Nutshell

Edge Computing and Artificial Intelligence popularly also known as Edge AI or AI on the Edge is projected to grow at a rapid rate making a significant impact on almost every industry. This is why businesses are now banking on the power of both these technologies as a part of their digital transformation journey. Edge computing along with the cloud has been giving new lease to data storage and processing solutions. With the addition of AI, the process has become more efficient. Therefore, fusing Edge Computing and AI is indeed the next step towards a brighter future in computing for businesses. 

The future is edge AI, and the best way to get there is to support it with cloud computing. Calsoft possesses the expertise and experience to effectively implement AI/ML services, Edge Computing, and cloud technology to meet evolving technological demands. Our solutions are curated to derive value and make a difference. To know more get in touch with us now.

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