Edge AI in Machine Learning

Edge AI begins with Edge Computing. It is also known as edge processing, edge computing is a network technology that positions servers locally near devices. In Machine Learning Edge AI helps reduce system processing load and resolve data transmission delays. These processes are performed at the location where the sensor or device generates the data, also known as an edge.

Developments in advanced computing mean that cutting edge AI is becoming more and more important. This is true in various industries, especially when it comes to processing latency and data privacy. In this article, I’ll walk you through the impact of Edge AI, why it’s important, and common use cases.

Also, Read – Understanding the Main Parts of a Robot.

What is Edge AI?

Edge AI refers to Machine Learning algorithms that process locally on hardware devices and can process data without a connection. This means that operations like data creation can happen without streaming or storing data in the cloud. This is important because there are a growing number of cases where device data cannot be managed through the cloud. 

We can see a good example of this technology at work in the robots working in a Factory. Artificial Intelligence can be used here to visualize and evaluate large amounts of multimodal data from surveillance cameras and sensors at speeds that humans cannot process. 

We can also use it to detect erroneous data on production lines that humans might miss. These types of IoT structures can store large amounts of data generated from production lines and perform analysis with machine learning. They are also at the heart of deductive and predictive models that improve the smart working of factories.

Why Edge AI is Important?

There are a growing number of cases where device data cannot be managed through the cloud. For instance, an autonomous car suffering from a cloud latency while detecting the objects on the road or while operating the brakes or maybe while operating the steering wheel. Any slowdown in data processing will result in a slower vehicle response. If the deceleration is such that the vehicle does not respond in time, it may lead to an accident. Lives are literally in danger.

For these IoT devices, real-time response is a necessity. This means the ability for devices to analyze and evaluate images/data onsite without relying on cloud AI.

Use Of Edge AI

Currently, the advanced Artificial Intelligence market in terms of Edge AI is primarily comprised of two areas: industrial machinery and consumer devices. We are seeing progress with demonstration testing in areas such as equipment control and optimization and the automation of skilled labour techniques.

Advances are also being made with consumer devices equipped with AI cameras that automatically recognize photographic subjects. As the number of devices is larger than the number of industrial machines, the market for consumer devices is expected to grow significantly from 2021.

It’s Future in Machine Learning

According to the “2019 AI Business Aggregate Survey” published by Fuji Keizai Group, the advanced computing market in Japan had an expected market size of 11 billion yen for fiscal 2018.

The survey predicts that the market will reach 66.4 billion yen in the fiscal year 2030. And with the spread of 5G, we will also likely see lower costs and growing demand for cutting-edge AI services in the whole world.

Also, Read – Machine Learning project on Prediction of Fuel Efficiency.

I hope you liked this article on an introduction to Edge AI in Machine Learning. You should know about it because it’s the future of Machine Learning. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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