What is Edge AI?
Edge AI is a type of edge computing where artificial intelligence (AI) applications are deployed directly to devices located at the network’s edge. Under this approach, each device collects and processes data locally without sending it back to a centralized location such as the cloud or a private data center.
At a high level, edge AI enables remote devices to make inferences from local data in real time with minimal latency.
Why Do We Need Edge AI?
With the adoption of the Internet of Things (IoT) and smart devices estimated to grow from 15.1 billion in 2023 to 34.6 billion in 2023, edge AI is emerging as a popular framework to collect and process data efficiently at the network’s edge.
Under an edge AI approach, AI models can be deployed directly to devices, which then collect and process data locally. This gives them the ability to draw inferences and develop insights without needing to connect to the Internet or a centralized AI model.
Decentralized processing also means that insights can be generated in real-time with less latency than if the device had to send data to the cloud to be processed and listen for a response.
The efficiency of edge AI makes it a natural fit for environments where organizations want to put themselves in a position to process the data collected by IoT and smart devices.
Moving AI inference to the network’s edge also enables organizations to make sure that legally protected data categories, such as personally identifiable information (PII) aren’t exposed to the servers of cloud service providers and other third parties, which helps ensure compliance with local and international data protection regulations.
The Role of Cloud Computing in Edge AI
Leveraging cloud computing is essential for unlocking some of the main benefits of edge AI. While the two are distinct concepts, they can be mutually beneficial when training AI models.
For instance, an organization can train a centralized model in the cloud and ship that to devices. This model can then be periodically retrained by data collected from the network’s edge, and the updated model can then be shipped to downstream devices.
Likewise, the cloud can step up to process data in those scenarios where edge processing doesn’t make sense. If an organization needs to process a high volume of information or complete inference tasks with a high computational requirement, then the scalability offered by the cloud makes this an ideal choice.
On the other hand, if an organization needs real-time processing and insights provided to end-users instantly via their devices, then edge AI is the better choice to keep latency to a minimum.
What are the Benefits of Edge AI?
Moving AI processing to the edge of a network provides some key benefits to enterprises. These include:
- Developing insights in real-time: Gathering and processing data locally enables AI models to provide user devices with real-time insights.
- More efficient processing: Processing, analyzing, and storing data locally increases efficiency, so you can process more data in less time without sending it to a central cloud server.
- Reduced power consumption: Inference tasks require less computational resources and consume less overall power.
- Reduced cost: More efficiency not only cuts spending on power but also requires less network bandwidth.
- Greater Privacy: Processing data locally reduces its exposure to third parties, such as cloud service providers, and lowers the chance of data leakage.
- High availability and reliability: Decentralization means that devices don’t need to be connected to the internet to continue processing data and collecting insights, which makes them less prone to downtime.
Examples of Edge AI Use Cases
Edge AI can be used in a wide range of scenarios. Some of the most common use cases for edge AI are listed briefly below:
- Virtual assistants: Edge AI can be used to power virtual assistants like Siri and Google Assistant to answer user’s questions and perform commands on demand.
- Smart devices and wearables in healthcare: Healthcare organizations can provide patients with wearable smart devices, which use biosensors to collect data on their heart rate, blood pressure, and sleeping patterns to help inform future treatments.
- IoT devices for preventative maintenance in manufacturing: Manufacturing companies can install sensors on machinery and equipment in factories and warehouses to predict potential failures and generate alerts so that an engineer can fix the issue before there’s any downtime.
- Self-driving robots: Organizations can install sensors in autonomous vehicles, drones, and automatic guided vehicles (AGV) to provide real-time insights into where the vehicle is going and where it’s located and even to provide data signals that can guide self-driving cars.
Edge AI makes it possible for organizations to bring together insights from devices that are located outside of the traditional enterprise network.
Companies looking to collect and process data from IoT and smart devices will need to embrace edge AI approaches if they want to derive the maximum value possible from the data gathered by these resources.