Define the new internet.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
5,641 definitions
Inference Data Split is a ml experimental control that separates examples for training, validation, and testing for model prediction serving. It uses randomization rules, leakage checks, and seed tracking so teams can measure generalization honestly while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for model prediction serving. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model prediction serving. It uses batch jobs, backfills, and index validation so teams can keep retrieval results current while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Evaluation Harness is a ml test system that runs repeatable checks against model behavior for model prediction serving. It uses fixtures, metrics, thresholds, and regression reports so teams can compare releases with evidence while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Fallback Path is a ai resilience pattern that keeps an AI feature useful when a provider or tool is unavailable for model execution for user or system requests. It uses degraded states, deterministic responses, and operator notices so teams can avoid fake AI success while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Feature Store is a ml service that serves consistent features to training and inference for model prediction serving. It uses versioned feature definitions, freshness checks, and access policies so teams can avoid training-serving skew while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Grounding Check is a ai quality control that verifies that generated answers are backed by available sources for model execution for user or system requests. It uses citation checks, retrieval evidence, and contradiction detection so teams can reduce unsupported claims while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for model execution for user or system requests. It uses risk scoring, review UI, and audit logs so teams can keep protected decisions accountable while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for model prediction serving. It uses bounded search spaces, trial tracking, and early stopping so teams can find better configurations while keeping evidence, reliability, and public-safe operational boundaries clear.
Inference Instruction Boundary is a ai policy boundary that separates durable system instructions from user-provided content for model execution for user or system requests. It uses role labels, precedence rules, and prompt assembly checks so teams can avoid instruction confusion while keeping evidence, reliability, and public-safe operational boundaries clear.