Edge computing for AI on the Internet of Things

Edge Computing for AI: Technology evolves quicker than ever in this connected world— a few years ago, many companies were struggling with the concept of cloud and moving their operations from their datacenter to the public cloud. However, cloud adoption has become a significant driver of business disruption today. If there’s any lesson to derive from this cloud transformation, it is that business will never operate in the older ways again. Similarly, companies should now prepare for the next renaissance motivated by edge computing. Like the transformation to the cloud, edge computing is creating vast new opportunities and changing the entire approaches to business.

A Gartner study states, “About 10% of enterprise-generated data is created and processed outside an on-premise data center or cloud. This figure will reach 75% by 2022” As this transformation happens the role of edge computing becomes significant and may have a profound impact on a company’s Information systems, and how companies build new products using these systems.

Internet Of things wave has swept across every business vertical bringing lot of efficiency and process improvements. The business has harnessed the power of IOT to reduce downtown, increase operational efficiency, improve customer service, enhance risk management, and enable new products and services, more importantly, help in prediction. Primary enabler of all these improvements is the fact that IOT creates intelligence out of the data generated by its sensors. This intelligence, in return, drives the improvements.

Edge computing is moving the resource closer to where the data is generated. There are potential benefits of moving the data closer to the edge than pushing all the data to the cloud. Edge computing has got much traction in recent times, and many companies have introduced products which are designed for edge computing. One of the most significant driving factors is that it reduces the overall latency of churning the data. Also, it can reduce the bandwidth utilization of the network and even and has the potential to reduce the cost. It also has another significant benefit, the security of data. In business who do not wish to move data to the cloud but want to leverage the benefits of edge computing can adopt edge computing.

Before the vast penetration of edge computing, AI and machine learning were indisputably tied to the cloud. However, now, with edge computing, it is time for the paradigm shift of where should AI and ML should be running. Having AI closer to the edge has its potential benefits. AI process the data, analyses to find a pattern, and then deep learning and ML is applied and then based on the review, it can trigger action. By marrying AI to Edge computing, It allows doing AI on real-time data and the ability to produce a quick response to IOT incident. This also provides excellent benefits in terms of security.

Scope for AI on Edge

The adoption of AI in Edge computing has not been a hockey stick yet because of the higher cost of implementation and the challenges in infrastructure. Data host, data processing, AI are all inevitably build on the highly available data center with the high processing power. Running AI kind of power hungry applications on these high power system does not throw a challenge. However, when we are talking about moving these to the edge, we need to consider the fact that the edge devices were built as low power devices with low processing power. So considering building an AI engine on these devices are not an easy task.

The path forward is to build edge devices with enough processing power. Also, the AI engines should be optimized to run on the edge devices, which means it should be made with the assumptions that it needs to run on systems with limited resources and should have faster performance.

Companies have started tackling these market needs. NVIDIA has recently launched EGX an edge computing platform with cloud and AI capabilities. Intel has a range of products focused for edge computing. Similarly AI engines from IBM.

Edge Computing for AI does not mean the death of the AI on the cloud. They need to co-exist. There are situations where data AI cannot solely rely on edge data. It might have to combine data from a different location. In these scenarios, the AI should be performed on the cloud. The success of this lies how companies build hybrid systems that work in harmony to carry the benefits of IOT, edge computing, and AI.

Footnotes:

  • Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.
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