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 "Training Provenance Ledger": Training Provenance Ledger is a ml record that tracks where data came from and how it changed for model learning and optimization workflows. 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 Training Provenance Ledger when the training job restarted, so the team could audit model inputs reliably before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Training Drift Monitor": Training Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for model learning and optimization workflows. 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 Training Drift Monitor when the training job restarted, so the team could respond before quality drops before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Data Split": Feature Data Split is a ml experimental control that separates examples for training, validation, and testing for input signals used by a machine learning model. 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.
“Beispielentwurf: The machine learning team used Feature Data Split when a feature distribution shifted, so the team could measure generalization honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Calibration Curve": Feature Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for input signals used by a machine learning model. It uses bucketed predictions, reliability diagrams, and threshold analysis so teams can make confidence scores useful while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Feature Calibration Curve when a feature distribution shifted, so the team could make confidence scores useful before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Model Card": Feature Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for input signals used by a machine learning model. It uses dataset notes, metric tables, and risk statements so teams can publish model behavior honestly while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Feature Model Card when a feature distribution shifted, so the team could publish model behavior honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Label Review": Feature Label Review is a ml quality workflow that checks annotations for consistency and usefulness for input signals used by a machine learning model. It uses agreement metrics, reviewer queues, and adjudication so teams can improve supervised learning data while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Feature Label Review when a feature distribution shifted, so the team could improve supervised learning data before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Embedding Refresh": Feature Embedding Refresh is a ml index workflow that updates vector representations after source data changes for input signals used by a machine learning model. 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.
“Beispielentwurf: The machine learning team used Feature Embedding Refresh when a feature distribution shifted, so the team could keep retrieval results current before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Training Checkpoint": Feature Training Checkpoint is a ml recovery artifact that saves model state during learning for input signals used by a machine learning model. It uses weights, optimizer state, and run metadata so teams can resume or inspect training safely while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Feature Training Checkpoint when a feature distribution shifted, so the team could resume or inspect training safely before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Hyperparameter Sweep": Feature Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for input signals used by a machine learning model. 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.
“Beispielentwurf: The machine learning team used Feature Hyperparameter Sweep when a feature distribution shifted, so the team could find better configurations before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Feature Bias Audit": Feature Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for input signals used by a machine learning model. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Feature Bias Audit when a feature distribution shifted, so the team could surface fairness risks before the model moved into evaluation.”