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.
2,337 definitions
Automatischer Uebersetzungsentwurf (German) for "Feature Evaluation Harness": Feature Evaluation Harness is a ml test system that runs repeatable checks against model behavior for input signals used by a machine learning model. 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.
“Beispielentwurf: The machine learning team used Feature Evaluation Harness when a feature distribution shifted, so the team could compare releases with evidence before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Feature Store": Feature Feature Store is a ml service that serves consistent features to training and inference for input signals used by a machine learning model. 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.
“Beispielentwurf: The machine learning team used Feature Feature Store when a feature distribution shifted, so the team could avoid training-serving skew before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Provenance Ledger": Feature Provenance Ledger is a ml record that tracks where data came from and how it changed for input signals used by a machine learning model. It uses hashes, source labels, and transformation history so teams can audit model inputs reliably while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Feature Provenance Ledger when a feature distribution shifted, so the team could audit model inputs reliably before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Drift Monitor": Feature Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for input signals used by a machine learning model. 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.
“Beispielentwurf: The machine learning team used Feature Drift Monitor when a feature distribution shifted, so the team could respond before quality drops before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Routing Human Approval": Routing Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for selection among models, tools, and workflows. 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.
“Beispielentwurf: The AI platform team used Routing Human Approval when the router selected a cheaper model, so the team could keep protected decisions accountable before the agent workflow reached production.”
Automatischer Uebersetzungsentwurf (German) for "Routing Response Schema": Routing Response Schema is a ai output contract that requires model output to match a known structure for selection among models, tools, and workflows. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The AI platform team used Routing Response Schema when the router selected a cheaper model, so the team could make responses machine-readable before the agent workflow reached production.”
Automatischer Uebersetzungsentwurf (German) for "Routing Fallback Path": Routing Fallback Path is a ai resilience pattern that keeps an AI feature useful when a provider or tool is unavailable for selection among models, tools, and workflows. 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.
“Beispielentwurf: The AI platform team used Routing Fallback Path when the router selected a cheaper model, so the team could avoid fake AI success before the agent workflow reached production.”
Automatischer Uebersetzungsentwurf (German) for "Routing Agent Trace": Routing Agent Trace is a ai observability record that captures the steps an AI workflow took for selection among models, tools, and workflows. It uses trace identifiers, tool events, and redacted metadata so teams can debug agent behavior without exposing secrets while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The AI platform team used Routing Agent Trace when the router selected a cheaper model, so the team could debug agent behavior without exposing secrets before the agent workflow reached production.”
Automatischer Uebersetzungsentwurf (German) for "Routing Memory Scope": Routing Memory Scope is a ai state boundary that limits what an assistant may remember or reuse for selection among models, tools, and workflows. It uses retention policies, consent checks, and namespace separation so teams can prevent accidental cross-context leakage while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The AI platform team used Routing Memory Scope when the router selected a cheaper model, so the team could prevent accidental cross-context leakage before the agent workflow reached production.”
Automatischer Uebersetzungsentwurf (German) for "Routing Safety Filter": Routing Safety Filter is a ai policy control that detects content that should be blocked, rewritten, or escalated for selection among models, tools, and workflows. It uses classifiers, rules, and human review queues so teams can keep outputs public-safe while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The AI platform team used Routing Safety Filter when the router selected a cheaper model, so the team could keep outputs public-safe before the agent workflow reached production.”