{"id":110655,"date":"2023-10-07T09:31:52","date_gmt":"2023-10-07T09:31:52","guid":{"rendered":"https:\/\/www.techopedia.com\/?post_type=definition&p=110655"},"modified":"2023-10-07T09:31:52","modified_gmt":"2023-10-07T09:31:52","slug":"edge-ai","status":"publish","type":"definition","link":"https:\/\/www.techopedia.com\/definition\/edge-ai","title":{"rendered":"Edge AI"},"content":{"rendered":"

What is Edge AI?<\/span><\/h2>\n

Edge AI is a type of <\/span>edge computing<\/span><\/a> where artificial intelligence<\/a> (AI) applications are deployed directly to devices located at the <\/span>network\u2019s edge<\/span>. Under this approach, each device collects and processes data locally without sending it back to a centralized location such as the <\/span>cloud<\/span><\/a> or a private data center.\u00a0<\/span><\/p>\n

At a high level, edge AI enables remote devices to make inferences from local data in real time with minimal latency.\u00a0<\/span><\/p>\n

Why Do We Need Edge AI?<\/span><\/h2>\n

With the adoption of the Internet of Things<\/a> (<\/span>IoT<\/span>) and <\/span>smart devices<\/span><\/a>\u00a0<\/span>estimated<\/span> to grow from 15.1 billion in 2023 to 34.6 billion in 2023<\/a>, edge AI is emerging as a popular framework to collect and process data efficiently at the network\u2019s edge.\u00a0<\/span><\/p>\n

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 <\/span>Internet<\/span><\/a> or a centralized AI model.\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

Moving AI inference to the network\u2019s edge also enables organizations to make sure that legally protected data categories, such as personally identifiable information<\/a> (<\/span>PII<\/span>) aren\u2019t exposed to the servers of cloud service providers and other third parties, which helps ensure compliance with local and international <\/span>data protection<\/span><\/a> regulations.\u00a0<\/span><\/p>\n

The Role of Cloud Computing in Edge AI<\/span><\/h2>\n

Leveraging <\/span>cloud computing<\/span><\/a> 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. <\/span>
\n<\/span>
\n<\/span>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.\u00a0<\/span><\/p>\n

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.\u00a0\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

What are the Benefits of Edge AI?<\/span><\/h2>\n

Moving AI processing to the edge of a network provides some key benefits to enterprises. These include:\u00a0<\/span><\/p>\n