As the IoT devices are escalating, enterprise data volumes are also multiplying. Conventionally, the sensitive data gathered by IoT devices has been largely stored in the cloud but as the latency introduced between data centers and end-users, the arrangement has become baseless. The organizations can find it difficult to rely on remote servers to process their data while dealing with critical operational needs. Apart from this cloud data security is also a complicated concern.

Due to these concerns, several enterprises are looking to the edge where they need to process data locally to assist real-time decision making. Substantially, they need faster processing than the cloud allows.

AI and Edge for IoT Applications

Edge computing technology enables automated decision making in less time possible. It enables seamless data collection from IoT devices and supports real-time decision-making locally.

AI at the edge offers a strengthened computing approach using a compact architecture. This approach drives local data-informed decision-making. Being smart and expensive at the same time, it can process and store a huge quantity of data locally while eliminating the need to do so elsewhere.

Thus, edge computing is applicable to enterprises internationally.

In terms of unit volumes, some common AI-enabled edge devices are – head-mounted displays, smart automotive sensors, consumer and commercial robots, drones and security cameras.

The technology can also extend to incorporate the processing power of PCs and tablets, mobile phones and next-gen smart speakers. Tech giants including Microsoft, Google, Amazon, and others have keenly invested in experimenting with solutions for AI-enabled edge computing solutions.

Need to Deploy AI Model on Edge Devices

While using edge computing, there remains no need for transferring data to the cloud for processing. Therefore, it eliminates the issue of latency. Subsequently, it accelerates the real-time decision-making of an enterprise.

The technology allows users to store, process and derive intelligence from data locally which results in building robust IoT solutions on-premises. Through real-time information from edge computing, AI can ensure constant processing by preventing sudden machine failures. Also, the parameters of edge AI when connected with IoT devices can detect the need for predictive maintenance.

It can avoid the security threats of the public cloud and keeps sensitive data in the local IT ecosystem. Moreover, an AI solution can identify anomalies at the edge of the network in case of cyber-attacks. AI-driven risk analysis detects every probable point of entry for cyber attackers. It also proactively creates plans to reduce security problems.

Conclusion

In contrast to the powerful AI apps that needed a huge and expensive datacenter to function, an AI-enabled edge computing device can function anywhere. However, edge computing will not be a replacement for cloud computing. As the digitally-enabled world is becoming more interconnected, it is unarguable that AI at the edge caters various opportunities that can help enterprises to drive operations efficiently while increasing productivity.