Edge AI: Build Your Edge Deployment Strategy

Edge AI is another step towards bringing together the physical and digital worlds. Already today it's revolutionizing the deployment and operation of machine learning models. And the market is projected to expand at a CAGR of over 24% through 2033, reaching $163 billion in value.

Edge AI can help solve business challenges quickly and effectively, especially when combined with cameras or vision sensors. Edge AI is deploying machine learning models directly to hardware devices in the field, such as GPUs, where the data is processed locally and in real time. By running models "at the edge," companies can benefit from high performance, fast decision making, reduced cloud costs, and enhanced security. One common use you might be aware of is in autonomous vehicles for real-time navigation and object detection.

Whether it's for computer vision, remote sensing, or industrial applications, edge AI offers a range of advantages. However, the complexities of hardware management and model updates can be a challenge. It's critical to carefully consider your edge deployment strategy, and that's where Roboflow can help. We provide all the tools - software, compute, cameras – you need to deploy cutting-edge vision AI. Learn more about vision AI at the edge.

What Is Edge AI?

Edge AI is deploying AI to a piece of hardware that exists out in the field - typically a GPU - and that GPU runs the model right then and there on-site, without sending the data anywhere else. So "at the edge" means the model is running in a self-contained way, on a specific piece of hardware for the purpose of your application. An example of one of those pieces of hardware might be a NVIDIA Jetson or Luxonis OAK. In short, deploying to the edge means having a model that can run offline and in real time.

Edge AI deployment is popular because of its performance (devices with edge AI can process data in just milliseconds), privacy (some companies may not want their images and videos uploaded to the cloud), and ease of use for when you don't have Internet connectivity. It comes as no surprise that Gartner is forecasting nearly 12 billion edge devices by the end of 2025.

Keep in mind, of course, it's also useful to have the ability to improve the model you're using. So you may choose to send some data back to the cloud, albeit with much less frequency (a hybrid approach). Even if you're not doing that, it is helpful to monitor model health - for example, knowing if an edge device is live, is it on or off, is it successfully making detections, etc - is important.

Advantages of edge AI:

Cloud compute expenses and bandwidth costs are reduced by processing data locally.
Low latency AI processing without waiting for transferring data to cloud delivers instant decision making.
You can deploy across thousands of devices without overloading cloud infrastructure.
Critical workloads continue running when Internet outages occur.
You can control exactly what data leaves the facility, so it's much less likely there will be data breaches and it's easier to ensure compliance with data regulations.

Disadvantages of edge AI:

Because the model is running in an offline environment it might be more difficult to monitor model health.
Also it can be challenging to do development for the edge.
Edge hardware introduces its own complexity, because the hardware on the edge could fail, you have to purchase, and manage the hardware.
You also have to have a way of updating the model and there are many more ways it can go wrong.

Edge AI Examples and Use Cases

Edge AI is increasingly used in remote and industrial settings.

In the field: If you're working on an application in agriculture, you might have a model that runs completely disconnected in a field and helps to identify weeds or crop growth.

In the yard: You might have a model that's running on a drone without internet connectivity. But when the drone sees things below, you want it to capture images or do some business logic with the data it receives, to enhance your logistics visibility. For example, you can track container yard inventory, scan and detect missing labels, and monitor equipment condition and ensure maintenance procedures are executed correctly.

In the factory: Some manufacturers deploy edge AI in their factories to assist with defect detection, using a combination of hardware and their own intranet. Using machine vision they're able to verify that when the goods they're producing come off the assembly line they're of the quality they want - identifying nuanced variations in product specifications such as color, texture, inaccurate labels, and other slight imperfections, ensuring nothing malformed goes to customers.

Achieving High Throughput With Edge AI

For edge deployment, getting high throughput (i.e. getting high frames per second) is often a key consideration, and that's based on the model's size, as well as the hardware available to the model. The Luxonis OAK can often achieve about 30 frames per second when deployed. NVIDIA Jetsons come in different levels of compute - Nano, Xavier, NX - in that order of computation power, and frames per second differs on which you're using.

There are optimizations that can be made to ensure performant models - including a number of frameworks to optimize how models run on given frameworks. So for example, for Intel devices models can be optimized using a framework called OpenVINO. For NVIDIA perhaps using TensorRT.

How Does Edge AI Work?

The model runs on a standalone device and processes data directly on the device. It can be stored on the device or connected to other systems to send data off the device. Here's how:

Deploy the model to edge hardware.
Many edge devices are designed for low-power consumption, making them suitable for battery-powered applications such as drones, cameras, and IoT sensors.
Then data is processed on the device in real time, and decisions are made locally.
Updating edge AI models requires strategies such as over-the-air updates.
Remote monitoring tools help track edge device status, including uptime, inference success rates, and potential failures.

Why Edge AI Now?

Models are getting better and smaller which means they are faster to run and better at solving more complex problems. Also, compute is getter smaller and better as well which means larger models can run faster than ever before. Finally, cameras are everywhere and getting better which means higher resolution visual data at a lower cost. All of this is coming together at the same time to unlock new use cases that were never before possible and doing so at precipitously dropping costs.

Edge AI vs Distributed AI

Distributed AI is when multiple models in multiple places all work together to create a larger system. It focuses on collaboration between distributed nodes: data is shared and processed across multiple devices or agents in a network, allowing for parallel problem solving. This could mean an edge device model sends data to a server at the facility which then connects to a server in the cloud - all working together to solve problems.

Get Started with Edge AI

Learn more about combining vision AI with existing infrastructure to solve business challenges quickly and efficiently. Talk to a machine vision expert.