Edge AI applications and solutions range from smartwatches and production lines to logistics and smart buildings and cities to smart cities and smart buildings. Because of its ease and variety of applications, edge AI is a popular choice for many organizations.
These are the top 10 trends in edge AI that will be prevalent in 2022.
Although edge computing has become a necessity for many businesses, deployments are still in their early stages. IT departments will be responsible for edge AI management to move from development to production.
IT departments will use cloud-native technology to address edge computing issues such as security, manageability, and scale.
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The edge of AI deployments has been dominated by computer vision. Image recognition was the first to use image recognition in AI training.
This resulted in a strong ecosystem of computer vision applications. NVIDIA Metropolis is an application framework and set o developer tools that allow you to create computer vision AI applications. It has now grown 100-fold to include more than 1,000 members.
Another space that is being driven by AI applications is the intelligent factory. Factories can add AI applications to cameras and other sensors that allow for inspection and predictive maintenance. But detection is only the first step.
Once an issue has been identified, it is time to take action. AI applications can detect anomalies and defects and alert humans to correct them.
For safety applications or other situations where immediate action is needed, the AI inference app can be connected to the IoT platforms, which manage the pick-and-place machines, robotic arms, and assembly lines. Custom development is required to integrate these applications.
Expect more partnerships between AI platforms and IoT management platforms, which will make it easier to adopt edge AI in industrial settings.
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AI-on-5G is a combined computing infrastructure that combines sensors, computing platforms, and AI applications.
It provides high-performance, secure connectivity to allow integration of sensors, computing platforms, and AI applications in the field, in the cloud, and on-premises. The key benefits include extremely low latency in non-wired environments and guaranteed quality of service.
MLOps are key for organizations that deploy edge AI. They will help drive data flow to and from the edge. MLOps are key to ensuring that edge AI is able to access new data and insights, train models and test them before deploying them to the edge.
This improves the accuracy of the model and the results. While updates can be made on an annual or quarterly basis for traditional software, AI benefits from continuous updates.
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The rise in global edge data centers is one of the most important Edge AI trends for 2022. Edge AI predicts that there will be more than five million servers deployed at the edge of the world by 2024.
The 5G network, IoT proliferation, and data gap will all increase the number of edge data centers. Because of its low latency and the availability of data storage close to the end-user, the demand is greater.
From the user terminal service to the display of 3D videos, the process sequence must be completed within milliseconds. This is necessary to avoid VR sickness when VR applications produce 3D video in real time. Edge computing is capable of providing high-quality, low-latency VR.
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Although we don’t like to see endless advertisements, it is more appealing when they are for good. Edge computing, which is based on real-time targeting data will enable a programmatic Digital Home marketplace (DOOH).
This allows the advertising ecosystem to connect to deliver advertisements. AI allows advertisers to be smarter by segmenting the audience’s demographics, sentiment, and location in front of the camera. The most popular edge computing trend of 2020 is the change of ads.
The edge computing transition would simplify client processes from development to implementation at edge. Edge ops are the name of this process.
Developers of all levels will be able to make the most of the edge’s power and improve their existing applications. This creates a new category of native edge applications.
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Digital twin computing is a method that allows for the creation of digital twins between humans and things. It can be used in many industries to perform computations in any combination.
The digital twin requires high bandwidth and thousands of sensors to relay data. The digital twin can transmit smaller amounts of data, and edge computing will perform some of the processing on-site.
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