The Paradigm shift from Edge to Edge AI. What are the drivers bringing this change?

The Paradigm shift from Edge to Edge AI. What are the drivers  bringing this change?

With the rising demand and need for a better experience, companies today are breaking walls to create uninterrupted, exceptionally fast and immersive abilities using AI in our everyday devices. In a nutshell, AI has changed the way computation is done in devices today. With the power of AI, both users and devices have become smarter than ever before.

Intelligent devices powered by AI & machine learning are everywhere: from chatbots to autonomous vehicles, from smartphones to medical devices. This has given abilities to users beyond the imagination. With the advancement in technology, the computational methodology has also evolved from on-premise software to cloud technologies, from cloud to edge and now AI on Edge.

The new wave of AI today is Edge AI. The idea of Edge AI has its origins in edge computing and is being developed through the implementation of Edge-based complex machine learning models.

What are the Drivers of Edge AI:

1) Privacy – One of the biggest advantages of Edge AI is managing privacy. Users today are increasingly more cautious about their data – where it is being stored, how it is managed and how it is handled, etc. With Edge, AI companies have better abilities to provide control to users on how their personal data is handled.

2) Security – With the number of users, devices increasing drastically on the cloud, it is very difficult for companies to protect user data. The security threat although can be handled through firewalls and multiple layers of fences, but there is always a level of threat. With Edge AI, the user data can be stored locally thereby reducing the amount of risk or threats.

3) Latency – One of the biggest advantages and the most beneficial application of Edge AI is reducing latency in devices. With real-time processing happening nearer to the source within the device, the time required to process and respond reduces drastically. While in the case of the Cloud AI, a constant network connection is required to process instructions and provide output but with Edge AI, this would not be required. This also adds to reducing the dependency on external sources making it swifter.

4) Load Balancing – In order to work fast and provide swift responses, applications have to carry resiliency on increasingly distributed systems and there need to be multiple endpoints of load balancing. With Edge AI, the devices can harness the power of computing locally while having multiple endpoints nearer to the device, working efficiently

The future of Edge AI:

While companies today are making tech investments as part of their digital transformation journey, other forward-looking organizations and cloud companies see Edge AI as a new and open opportunity to innovate further.

With applications of Edge AI wide open, companies today have already implemented it in various industries like Consumer Market, Industrial, Automobiles, etc. But with further advancements in the years to come, it’ll play a crucial role in facial recognition, Industrial IoT, Emergency Medical Care and others. With some more time on the calendar, Edge AI will drive our everyday devices.

ENAP Studio  – Decentralizing AI to deliver AI on Edge

ENAP Studio is an Edge AI platform that helps developers leverage AI capabilities on edge devices and simplify their transformation journey. works with the vision to decentralize AI. It provides a platform that enables developers a one-stop solution and helps companies cut down the costs and time required to go to market.

ENAP Studio by is an end-to-end workflow solution, focused on improving the efficacy of AI algorithms and models for Edge devices. It has a fully-integrated modular workflow that allows developers to easily Train, Optimize and Deploy Edge AI Neural Networks.

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