Edge AI is running machine learning models directly on devices or local hardware instead of a cloud server. Phones, cameras, vehicles and factory gateways are typical hosts.
Edge computing is the practice of processing data close to where it is created or used, such as devices, local gateways or regional points of presence. It avoids sending everything to a central data centre or cloud region.
Inference is running a trained model to produce an output, as opposed to training it. In production this is where most of the cost, latency and scaling work of an AI feature sits.
Model quantization is reducing the numeric precision of a model's weights, for example from 16-bit to 8-bit or 4-bit values. The model then needs less memory and runs faster.
A small language model is a language model with relatively few parameters, built to run cheaply and quickly on a single server, device or private environment.
Computer vision is the field of AI that lets software interpret images and video. Typical tasks are classifying objects, detecting defects, reading text and tracking movement.
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