Edge Computing Market Size and Best Stocks To Buy

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In this article, we discuss the 10 best edge computing stocks to buy. If you want to skip our discussion on the edge computing market, head over to 5 Best Edge Computing Stocks To Buy

What is Edge Computing? 

Edge computing focuses on bringing computing power closer to where data is generated, rather than relying on a centralized cloud-based system. In simple terms, edge computing involves relocating a part of storage and computing capabilities from the central data center to close proximity of data sources. Rather than sending raw data to a central facility for analysis, computation is performed at the location where the data is generated and only the computed results, like real-time business insights or equipment maintenance predictions, are transmitted back to the central data center. Devices such as smart speakers, watches, and phones, which engage in edge computing by locally collecting and processing data, are already a part of our daily lives.

Contrary to what some may think, edge computing doesn't need a separate "edge network." It can work on single devices or routers. If a separate network is used, 5G is important, offering fast wireless connections. This enables cool applications like autonomous drones, remote surgeries, and smart cities. The edge network is useful when on-site computing is too expensive or complex, but fast responses are needed, and the cloud is too distant. Edge networks enable local computation, allowing devices at the edge (like sensors, cameras, or IoT devices) to process data and make decisions without the need to send all information to a centralized cloud for analysis.

The Intelligent Edge aims to provide faster, more reliable, and efficient processing for quicker decision-making and reduced dependence on centralized cloud systems, which may face issues like latency and bandwidth. However, deploying and managing edge computing infrastructures pose challenges due to technological complexity, overwhelming data volume, and interoperability issues. Gartner predicts that by 2026, at least 50% of edge computing deployments will involve machine learning. AI algorithms are well-suited for the data-rich environments of edge computing, enabling quick and accurate analysis of vast data sets. Integrating AI allows edge devices to independently process and act on collected data without constant communication with a central server. This integration enhances decision-making speed, crucial for applications like real-time inventory management or industrial equipment calibration.