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 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 "Model Drift Bias Audit": Model Drift Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for changes in model performance over time. 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 Model Drift Bias Audit when the live population changed, so the team could surface fairness risks before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Embedding Embedding Refresh": Embedding Embedding Refresh is a ml index workflow that updates vector representations after source data changes for vector representation of content or entities. 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 Embedding Embedding Refresh when the embedding index changed, so the team could keep retrieval results current before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Inference Calibration Curve": Inference Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for model prediction serving. 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 Inference Calibration Curve when the endpoint handled burst traffic, so the team could make confidence scores useful before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Metric Provenance Ledger": Metric Provenance Ledger is a ml record that tracks where data came from and how it changed for measurement of model behavior. 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 Metric Provenance Ledger when the metric changed after data cleanup, so the team could audit model inputs reliably 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 "Experiment Data Split": Experiment Data Split is a ml experimental control that separates examples for training, validation, and testing for controlled model comparison. 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 Experiment Data Split when the experiment showed a metric tradeoff, so the team could measure generalization honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Experiment Hyperparameter Sweep": Experiment Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for controlled model comparison. 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 Experiment Hyperparameter Sweep when the experiment showed a metric tradeoff, so the team could find better configurations before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Vector Data Split": Vector Data Split is a ml experimental control that separates examples for training, validation, and testing for numeric representation and similarity search. 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 Vector Data Split when the vector store returned close matches, so the team could measure generalization honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Vector Bias Audit": Vector Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for numeric representation and similarity search. 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 Vector Bias Audit when the vector store returned close matches, so the team could surface fairness risks before the model moved into evaluation.”