Edge computing puts power at the data source to process it instantaneously, while edge AI augments edge computing systems with real-time intelligence.
Edge computing is redefining data analytics and end point intelligence from millions of disparate devices worldwide. Internet-connected devices — AI-embedded IoT and a plethora of applications requiring real-time processing and analytics are driving edge computing like never before.
Edge computing focuses on decentralizing information from the cloud or a data warehouse and pushing it towards the network’s edge, towards the users. Hence a strong case of edge computing is being made by placing compute and analytics close to where the data is originally being generated. Add AI at the edge, and businesses see the spectrum of use cases broadening.
This is why smartphones are predicted to take precedence in edge computing due to their vast underutilized compute power. Today, users increasingly depend on mobile devices to carry out computing and storage-intensive tasks on their phones. For this to happen, these tasks need to be offloaded from the cloud to enhance performance. The objective is difficult to achieve and expensive unless the cloud is bought closer to the edge network, near the users. Mobile operators are intensively working on this concept (known as Mobile Edge Computing (MEC)), where base stations will have the ability to integrate computing, storage, and networking resources with mobile devices. Sensitive applications such as Augmented Reality and Image Recognition can then be hosted at the device level itself.
Many carriers worldwide are working on edge-computing strategies in their 5G deployments. The goal is to provide high bandwidth and a low latency along with faster real-time processing for mobiles and connected & self-driving cars. Providers are rolling out licensed edge services, which cost less than the hardware. The idea is to have live edge nodes near a provider’s base station and produce a spectrum for a hassle-free edge by using 5G’s network slicing feature.
The use cases of Edge AI and edge computing systems are far too many to list. However, every industry vertical is bound to find crucial applications in the form of use cases in edge computing systems and AI edge. Augmented Reality, Virtual Reality, Predictive maintenance, smart grids, autonomous vehicles, remote monitoring in oil and gas, patient monitoring, traffic management, and smart homes are some of the many use cases that encapsulate most industries where edge computing powered by AI can be leveraged.
AI embedded IoT use cases with edge computing will be taking center stage in the near future, with the IoT market set to reach $650.5 billion by 2026. Hence, organizations should adopt AI & edge computing-powered IoT for strong business cases and a comprehensive return on investment (ROI). Growing IoT and the real-time analysis of edge devices are creating new paradigms around security, cost control, and efficiency. For businesses that want to stay ahead of the competition and lead the market, this is a great value proposition. It has ushered in new horizons in viewing how and where information is accessed, leading to cost, performance, and efficiency optimizations for organizations.
Let’s look at how edge computing and edge AI helps businesses.
While edge computing acts opposite to the norm of data and application processing on a core platform, the importance of the cloud does not reduce with edge computing. In fact, both can work in tandem to create a highly efficient, seamless, and effective business model. Organizations can leverage the cloud’s big data capabilities, and the edge’s faster processing speeds when combined.
Companies that initially relied heavily on the cloud for bandwidth to run their applications will find edge computing a less expensive alternative. Since edge computing brings processing power and data closer to the devices where it is being gathered, rather than to a central repository that might be many miles away, cost savings is an immediate result.
One of the most significant benefits of edge computing is its ability to store and process data way faster when compared with cloud-based systems or data centers. The functionality is a massive enabler for efficient real-time applications, which is crucial to companies today, considering the pace of technology and consumer experiences.
The example of facial recognition can be cited here where, before AI edge, the process had to run the algorithm through a cloud-based system which was very time-consuming. As mentioned previously, facial recognition happens locally, in a gateway, on an edge server, or on the smartphone itself.
Find edge computing and edge AI intriguing? We are sure you are. However, the more you explore, the chances are you will find deployments complex, consisting of many layers of technological components. That’s why you need the expertise to build AI edge and edge computing systems from scratch. An arrangement wherein your partners establish a system while you focus on strategy to use this new system in the best possible way.
Cambridge Technology can be your perfect partner in establishing a robust edge computing ecosystem. With a range of specializations in IoT in AI, design, engineering, and development, we ensure a rapid transformation and immediate results. Integrating edge computing in IoT and other edge devices creates the ideal environment for businesses to harness their full potential. For the past 22 years, we have helped hundreds of companies adopt and leverage new technology to create strong revenue streams.
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