Edge AI vs Cloud AI

The differences between Edge AI and Cloud AI come into play primarily for machine learning and deep learning use cases. Deep learning algorithms require intensive processing, making hardware performance a significant factor.

Cloud AI can provide excellent performance, but most deep learning applications can’t compromise on latency in the data transfer process or security threats in the network.

For these reasons, Edge AI has become an appealing solution for artificial intelligence applications.

Before exploring the main differences between Edge AI and cloud AI, let’s define each concept.

What is meant by the terms Edge AI and Cloud AI?

To create a clearer picture of Edge AI, think about devices such as laptops, smart speakers, robots, drones, self-driving cars, and HomeCams that employ video analytics. With Edge AI, algorithms are processed on servers close to devices like these or directly on those devices themselves.

This localized or on-device processing allows devices to make decisions within milliseconds without needing an internet or cloud connection. Essentially, as a device produces data, the algorithms onboard can put the data to use.

Almost all workplaces have functions that benefit from the real-time processing that Edge AI offers. Edge AI knows no bounds regarding likely use cases and will continue to thrive over the coming years.

But what about Cloud AI?

Put simply, cloud technology provides computing services over the cloud. These computing services include access to analytics, databases, software, networking, servers, storage, and artificial intelligence.

Cloud AI is a concept that fuses artificial intelligence and cloud computing.

Cloud AI works by combining both AI software and hardware to provide businesses with access to AI while simultaneously empowering them with AI skills. As such, the AI cloud supports many AI projects and exciting use cases.

Cloud-based AI can predict situations, learn from whatever data is gathered, and figure out problems before they happen.

5 tradeoffs between Edge AI and Cloud AI

Firstly, Edge AI and Cloud AI are not the same things, nor are they technologies that can take the place of one another.

Instead, the two can work together, allowing businesses to capitalize on the strengths of each. For example, Cloud AI can work by processing data that’s not time driven, while Edge AI can work with more time-sensitive data.

Let’s explore 5 tradeoffs between Edge AI and cloud-based AI.

1. Energy consumption

IT professionals don’t usually worry about energy restrictions with Cloud AI, but they need to consider this with Edge AI. This is because AI models require a lot of processing power that isn’t always available at the Edge.

This problem is especially relevant when running deep learning algorithms due to their hefty processing power requirements.

Luckily, TinyML (machine learning specifically designed for the Edge) and specialized AI hardware known as AI accelerators can help by reducing the model’s size and power requirements, thus optimizing it for use at the Edge.

2. Connectivity issues

Consistent connectivity is essential to many AI use cases. Autonomous vehicles provide a good example. They need continuous connectivity to keep running safely. Edge AI provides this as it doesn’t require a stable internet connection to be perfectly functional.

3. Processing ability

Edge AI, on the whole, isn’t as easy to upgrade as cloud-based AI. This is a problem as timely updates impact an Edge-enabled device’s processing ability.

AI management platforms like Xailient’s Orchestrait can help address this issue by providing quick updates and a host of Edge AI management tools.

4. Latency considerations

One of the biggest concerns of Cloud AI is latency.

Data must be sent over long distances to centralized cloud servers when using the cloud to perform AI tasks. Sometimes, these servers can be thousands of miles away, resulting in delays.

Additionally, with the unprecedented amount of data being generated today, the cloud can become overloaded, suffering from bandwidth issues contributing to this problem.

With Edge AI, processing is completed on-device or as close to the device as possible, lessening or eliminating communication delays.

5. Security protection

Processing sensitive information is best done locally using Edge AI for improved security.

Indeed, Cloud AI provides a good amount of security, but data is most susceptible to hacking in transit. Therefore, keeping data local or entirely on-device is the best bet for protecting sensitive data.

Why organizations are turning to Edge AI

Enterprises are turning to Edge AI primarily for its costs and latency benefits.

Enterprises may prefer Edge AI over Cloud AI when considering the issue of latency. Aside from that, Edge AI is favored over Cloud AI when running AI workloads in remote locations. Edge AI is the clear choice in areas with little to zero internet connectivity.

Companies still use the cloud for most of their AI workloads despite this. They aim to gain the benefits of Cloud AI that can be applied to their operational processes. Specifically, organizations are interested in artificial intelligence, robotics, machine learning, IoT, virtual reality, and augmented reality.

When it comes to IoT specifically, as companies continue to invest in this technology, adding legacy assets and more and more sensors, their bandwidth load will quickly increase. Edge AI addresses this by delivering real-time insights with all processing taking place on the Edge-enabled IoT device itself.

With Edge AI, data generated by IoT devices won’t clog the network and can alleviate the traffic on local Wi-Fi, making Edge AI an excellent choice for IoT devices in business environments.

Put simply, Edge AI is growing in popularity as organizations seek to maintain their AI’s high performance while also considering security measures, latency issues, bandwidth, and costs.

Can the Edge and the cloud work together to deliver AI?

It doesn’t stand to reason that a choice must be made between Edge AI and Cloud AI in all cases.

Instead, by incorporating Edge AI and Cloud AI, enterprises are given a chance to make the most of both these technologies.

Edge AI serves as a platform to execute real-time decision-making tasks. In contrast, Cloud AI is a platform to facilitate the continuous learning of models to enhance performance and provide ongoing learning for AI.

Using the Edge and cloud for AI can be the best of both worlds. Who says you can’t have your cake and eat it too?