Nowadays, artificial intelligence technology such as machine learning and deep learning has invaded different applications, services, and devices. As a result, both individuals and companies have benefited in a number of areas.
Thus, the use of smart devices that optimize equipment functions, streamline industrial processes, detect anomalies, process natural language and, in short, facilitate personal and professional life is common. These devices are integrated into vehicles, telephones, security systems, etc.
However, this technology requires an improvement in its data processing, such as optimizing information transfer times or reducing the need for permanent connectivity. This gives rise to the need for machine learning at the edge, a new paradigm that promises great benefits.
Machine learning at the edge is a type of technology that allows smart devices to execute data processing locally, either through the device itself or by using local servers.
Therefore, this state-of-the-art machine learning refers to the use of algorithms, in both machine learning and deep learning, so that a system or device can process the information it has captured and obtain the response without delay, in real time. However, when necessary, the device can send the information to edge servers (local).
Until now, developments in the area of machine learning were associated with cloud servers with a large computational capacity.
Therefore, the technology in devices captured data from the environment and sent it to remote servers for processing. The response was then transmitted to the devices.
Consequently, the response and prediction ability was not immediate, but rather depended on the data transfer speed and the existence and security of connectivity.
In contrast, machine learning at the edge brings both processing and data storage closer to the device. This eliminates the dependency of such equipment on remote cloud servers.
Now, with machine learning technology at the edge, devices can analyze and process captured data, solve problems in real time and, at the same time, determine what information should be sent to the cloud to be processed by more powerful algorithms.
It is important to remember that devices, unlike powerful servers, have limited computational capabilities, therefore, a kind of hybrid computing is emerging: a combination of cloud and edge. In this way, each task is executed and solved in the most appropriate place.
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One of the features of machine learning at the edge is scalability. This means that this technology has the ability to adapt to different scenarios, so it can be applied to edge devices, edge servers, or in the cloud, according to available resources.
Applications of machine learning at the edge in devices include smartphones, ATMs, and smart cameras. These devices have small processors both for storing and processing data and for performing some analytical tasks.
Edge or local servers include the processors that are found in the operating rooms of banks or industrial sector companies. These devices have continuous communication with smart devices that they can help perform tasks.
Another example of the application of machine learning technology at the edge in devices are smart speakers such as Alexa, Google, or Siri. These devices have an algorithm trained to analyze and identify each of the words they detect. They can also store information locally. However, if one of these speakers has to solve a problem and does not have the right information, it must connect to the cloud and obtain the data.
There are also applications in the industrial sector. For example, some companies have predictive systems made up of sensors and algorithms to monitor the condition of facilities and provide timely notification of the need for preventive or corrective maintenance actions.
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Machine learning at the edge brings a total transformation of the way in which data is processed and provides the following benefits:
Facilitates real-time data processing.
Improves response and data transmission times.
Helps reduce the consumption of bandwidth resources associated with cloud processing.
Resolves security issues associated with mobile device and application users storing personal data in the cloud.
Contributes to the energy savings of mobile devices by allowing these end devices to offload certain tasks to local servers, which also leads to extended battery life.
Machine learning at the edge is a kind of extension of cloud services to bring them closer to the end user. It is a technology that is still under development, but that in the future will have applications in every area of society, including life-saving applications at medical centers.
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