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.
Autoscaling automatically adds or removes computing capacity, such as servers, containers or function instances, in response to demand. Applications keep up with traffic peaks without paying for idle resources during quiet periods.
MLOps is the practice of deploying, monitoring and updating machine learning models in production. It applies DevOps discipline to models and the data they depend on, so releases are repeatable, auditable and reversible.
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.
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.
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