model aggregation
Model aggregation combines access to multiple AI models in one product, interface, catalogue, or workflow.
AI Terminology
Search clear AI terminology definitions, acronyms, related concepts and the reviewed videos that explain them.
Showing 761–780 of 1130 terms
Clear filtersmodel 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.
model interpretability
Model interpretability is the ability to understand why an AI model produced a result and what internal features or processes influenced it.
model offloading
AI model offloading moves selected model weights or runtime state from accelerator memory to system memory or storage.
model parallelism
AI model parallelism splits one model's computation or parameters across multiple processors so they work together on the same workload.
model personality
Model personality is the recognizable tone, manner, and interaction style an AI model presents across conversations.
model pool
An AI model pool is a defined set of models eligible to handle a particular task or class of requests.
model portability
AI model portability is the ability to move a model and its workload between compatible hardware, runtimes or inference providers.
model post-training
AI model post-training adapts a pretrained model using additional examples, preferences, rewards or domain data after its initial large-scale training.