Machine Vision Users Begin Adopting Cloud Computing

Machine vision systems have always produced enormous amounts of image data in their daily operations. As systems increase in resolution, complexity, and speed, volumes of image data continue to increase. Today, many companies are beginning to turn to the power of cloud computing to handle these data flows. 

The storage of image data is an appealing aspect of the cloud, but recently internet service providers have improved upload speeds, making cloud computing an even more practical decision. While leveraging the cloud has many benefits for machine vision applications, end users are integrating it to varying degrees. 

Edge Computing and Cloud Computing in Machine Vision

Some machine vision users who are wary of storing data off-site in the cloud have been relying on edge computing. This is where data is processed at the edge of the network, where it’s actually being generated, instead of a centralized environment.

The primary advantage of edge computing is that it facilitates real-time data processing without latency, allowing users to respond to data as it’s being generated. However, there are several drawbacks when it comes to using edge computing, mainly that the volumes of data flow in today’s machine vision applications are simply too large to be handled entirely through edge computing. Additionally, the cloud can perform far more advanced computing functions that edge computing devices can. 

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Cloud Adoption on the Rise Among Machine Vision Users

Once machine vision users see the full potential of the cloud, they’re far more likely to invest in cloud computing. The earliest adopters of cloud computing are those using 3D vision for analysis, which generates large amounts of data. After that, it’s likely end users deploying deep learning and artificial intelligence in logistics, material science calculations, and preventative or predictive maintenance, will be the next major adopters of cloud computing. 

Each of these applications requires computing large amounts of data, but even beyond computing power, there are other benefits of the cloud. The vast storage capacity of the cloud allows machine vision users to retroactively test historical images to verify process quality and trace back product faults. 

Machine vision users are turning to the cloud to handle large amounts of data and to leverage advanced computing capabilities. As more and more machine vision users see the limitations of edge computing, the cloud will see a continued rise in adoption.