Processing data close to where it is generated, such as on devices or local servers, instead of sending everything to a distant data center.
Edge computing moves computation toward the edge of the network, near sensors, cameras, phones, and machines. This reduces latency, saves bandwidth, and can keep sensitive data local.
Consider a factory camera inspecting products for defects. Sending every video frame to the cloud would be slow and expensive, so a small computer on site runs the vision model and only reports results. Self-driving cars, smart retail stores, and remote industrial sites use the same principle. Edge computing usually works alongside the cloud rather than replacing it: the edge handles time-sensitive tasks, while the cloud handles heavy analysis, long-term storage, and model training. Managing many distributed edge devices securely is a major challenge.
Delivering computing resources such as servers, storage, databases, and software over the internet on a pay-as-you-go basis.
Federated LearningA machine learning approach where models are trained across many devices or servers without moving the raw data to a central location.
Internet of ThingsThe network of physical devices, from home appliances to industrial sensors, that connect to the internet to collect and share data.
LatencyThe time delay between a request being sent and a response beginning to arrive, usually measured in milliseconds.
On-Device AIAI that runs directly on a phone, laptop, or other device instead of sending data to remote cloud servers for processing.
WebAssemblyA compact binary format that lets code written in languages like C++, Rust, and Go run in web browsers at near-native speed.
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A plain-English guide to edge computing: how it differs from the cloud, where it actually pays off, what 5G changes, and how to start without overbuilding.
A plain-English guide to computer vision: how machines classify and detect objects in images, and where the technology already works in retail, factories and healthcare.