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 "Dataset Hyperparameter Sweep": Dataset Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for labeled and unlabeled data used for learning. 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 Dataset Hyperparameter Sweep when the dataset received a new batch, so the team could find better configurations before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Experiment Training Checkpoint": Experiment Training Checkpoint is a ml recovery artifact that saves model state during learning for controlled model comparison. 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 Experiment Training Checkpoint when the experiment showed a metric tradeoff, so the team could resume or inspect training safely before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Dataset Provenance Ledger": Dataset Provenance Ledger is a ml record that tracks where data came from and how it changed for labeled and unlabeled data used for learning. 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 Dataset Provenance Ledger when the dataset received a new batch, so the team could audit model inputs reliably before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Dataset Embedding Refresh": Dataset Embedding Refresh is a ml index workflow that updates vector representations after source data changes for labeled and unlabeled data used for learning. 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 Dataset Embedding Refresh when the dataset received a new batch, so the team could keep retrieval results current before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Experiment Bias Audit": Experiment Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for controlled model comparison. 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 Experiment Bias Audit when the experiment showed a metric tradeoff, so the team could surface fairness risks before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Metric Bias Audit": Metric Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for measurement of model behavior. 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 Metric Bias Audit when the metric changed after data cleanup, so the team could surface fairness risks before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Dataset Bias Audit": Dataset Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for labeled and unlabeled data used for learning. 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 Dataset Bias Audit when the dataset received a new batch, so the team could surface fairness risks before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Dataset Feature Store": Dataset Feature Store is a ml service that serves consistent features to training and inference for labeled and unlabeled data used for learning. 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 Dataset Feature Store when the dataset received a new batch, so the team could avoid training-serving skew before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Label Label Review": Label Label Review is a ml quality workflow that checks annotations for consistency and usefulness for ground-truth or weak-supervision annotation. 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 Label Label Review when the label set had disagreement, so the team could improve supervised learning data before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Model Drift Training Checkpoint": Model Drift Training Checkpoint is a ml recovery artifact that saves model state during learning for changes in model performance over time. 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 Model Drift Training Checkpoint when the live population changed, so the team could resume or inspect training safely before the model moved into evaluation.”