metered billing
Metered billing charges for measured consumption such as tokens, compute time, data, calls, or messages rather than one fixed upfront amount.
AI Terminology
Search clear AI terminology definitions, acronyms, related concepts and the reviewed videos that explain them.
Showing 721–740 of 1096 terms
Clear filtersminimum viable AI solution
A minimum viable AI solution is the smallest bounded system that can test whether AI improves a real outcome responsibly.
mobile AI workflow
A mobile AI workflow lets a user start, continue, review or control AI-assisted work from a phone or tablet.
mobile app testing
Mobile app testing verifies that an application behaves correctly, securely, and consistently across devices, operating systems, and real user flows.
mobile device automation
Mobile device automation controls phones or tablets through software to perform and verify repeatable actions without manual input.
model access agreement
A model access agreement defines the contractual conditions under which an organization may use, distribute, integrate, or resell access to an AI model.
model activation
A model activation is an intermediate numerical response produced inside an AI model while it processes an input.
model aggregation
Model aggregation combines access to multiple AI models in one product, interface, catalogue, or workflow.
model architecture
An AI model architecture is the structural design that defines a model's components, connections and flow of information.
model availability
Model availability describes whether customers can reliably access an AI model with enough capacity, uptime and plan allowance to complete their work.
model bias
AI model bias is a systematic tendency in a model's outputs that reflects imbalanced data, design choices, objectives or deployment context.
model compression
Model compression reduces an AI model's storage, memory or compute requirements while trying to preserve useful capability.
model consistency
AI model consistency is the degree to which a model produces reliably similar quality and requirement-following across comparable runs.
model controllability
AI model controllability is the degree to which users can reliably direct a model's behavior through instructions and settings.
model distillation
Model distillation trains a smaller or different AI model to reproduce useful behavior from a more capable teacher model or its outputs.
model drift
Model drift is a change in an AI model's real-world behavior or performance as models, data, users or operating conditions evolve.
model embedding layer
An AI model embedding layer converts discrete input identifiers such as tokens into numerical vectors the model can process.
model failover
AI model failover reroutes a request to another eligible model when the preferred model is unavailable or unhealthy.
model fingerprinting
Model fingerprinting compares distinctive input-output or tokenization behavior to infer whether AI systems may share an origin or implementation.
model governance
Model governance defines who may develop, evaluate, release, operate, change, and oversee an AI model.