Cloud AI vs Edge AI: Which is a Better Choice in 2022?

Edge AI and cloud AI are some of the most important technologies that are currently used by businesses

Over the past couple of years, the internet has gone through a massive transformation to keep up with the growing demands. Just a decade ago, companies had to maintain work infrastructure and funding for IT departments to keep the business operations running smoothly. But all these changed with the advent of cloud technology. In the modern business industry, the utilization of cloud computing platforms has made operations much more simpler and efficient for business managers to handle. Small and big companies are continually moving their applications to the cloud, and gradually more enterprises are realizing the importance of cloud technology.  Besides, experts believe that AI integrated computing techniques will overtake the IT industry. The advent of edge AI and cloud AI has deeply influenced the current state of how businesses handle sensitive data and applications. 

After the outbreak of the Covid-19 pandemic, the entire ecosystem of our work environment was disrupted as employees were forced to stay indoors and work from home. This phenomenon encouraged more adoption of disruptive technologies to battle the existing working conditions and carry out the operations smoothly. This changing paradigm has been extremely challenging, but the evolution of technologies like deep learning and AI has come to the benefit of industries like manufacturing, healthcare, IT, and others.

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The Rising Adoption of Edge AI

Edge AI addresses the processing and implementation of machine learning algorithms locally on the hardware. This form of local computing minimizes or eradicates network delay for data transfers or sharing and solves security challenges as everything takes place on the device itself. But this local processing does not mean that the training of the ML algorithms has to happen locally. Generally, the training takes place in a platform that possesses enhanced computational capacity to process larger datasets. The system is blended with AI accelerating features along with the deployed model for real-time data processing applications. 

In recent years, edge AI technology has undergone massive changes due to increased demand for GPUs, NPUs, TPUs, and other AI accelerators. Besides, the rise in the use of both machine learning and artificial intelligence will lead to increased use of edge AI applications and platforms for local high-level processing and computational capacity in the hardware. 

The Benefits of Cloud AI

Even though some experts believe that integrating AI in the cloud and edge technology is a complementary part of a larger strategy, everyone will agree that deploying artificial intelligence in computing has not only eased up conducting business operations but has also changed the financial strategies that are created by business organizations. 

An AI cloud infrastructure can support many projects and workloads simultaneously. A shared foundation focuses on bringing together AI hardware with open-source software that delivers AI capabilities with hybrid cloud services. Harnessing the power of artificial intelligence and cloud computing will enable organizations to carry out agile development. In today's rapidly changing technological world, companies are trying to stay a step ahead to maintain relevance with their customers. Furthermore, deploying cloud AI might be an excellent way for businesses to have faster development cycles to deliver solutions more efficiently while minimizing errors. 

Striking a Balance: Implementing a Hybrid Cloud Infrastructure 

Cloud AI supports processing in hardware by providing computational power remotely on the cloud. Since this processing takes place remotely, the system is more powerful in performance and processing. Besides, AI cloud computing increases the options concerning architecture and design. Edge AI is quite different from cloud AI, but it provides several other facilities which when combined with AI cloud can prove quite valuable.

For many companies, the convergence of cloud and edge is necessary. Hybrid cloud architecture allows enterprises to take advantage of the security and manageability of on-premises systems, also leveraging public cloud resources from a service provider. But deploying this technology might mean different things for different organizations. It might mean training in the cloud and deploying at the edge, training in the data center, and using cloud management tools at the edge. There are limitless opportunities for an organization when they bring together cloud and edge on the same platform.