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 Data Split": Dataset Data Split is a ml experimental control that separates examples for training, validation, and testing for labeled and unlabeled data used for learning. 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 Dataset Data Split when the dataset received a new batch, so the team could measure generalization honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Vector Feature Store": Vector Feature Store is a ml service that serves consistent features to training and inference for numeric representation and similarity search. 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 Vector Feature Store when the vector store returned close matches, so the team could avoid training-serving skew before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Label Bias Audit": Label Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for ground-truth or weak-supervision annotation. 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 Label Bias Audit when the label set had disagreement, so the team could surface fairness risks before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Training Bias Audit": Training Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model learning and optimization workflows. 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 Training Bias Audit when the training job restarted, so the team could surface fairness risks before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Model Drift Hyperparameter Sweep": Model Drift Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for changes in model performance over time. 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 Model Drift Hyperparameter Sweep when the live population changed, so the team could find better configurations before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Inference Hyperparameter Sweep": 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.
“Beispielentwurf: The machine learning team used Inference Hyperparameter Sweep when the endpoint handled burst traffic, so the team could find better configurations before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Label Model Card": Label Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for ground-truth or weak-supervision annotation. 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 Label Model Card when the label set had disagreement, so the team could publish model behavior honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Model Drift Label Review": Model Drift Label Review is a ml quality workflow that checks annotations for consistency and usefulness for changes in model performance over time. 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 Model Drift Label Review when the live population changed, so the team could improve supervised learning data before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Experiment Model Card": Experiment Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for controlled model comparison. 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 Experiment Model Card when the experiment showed a metric tradeoff, so the team could publish model behavior honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Experiment Evaluation Harness": Experiment Evaluation Harness is a ml test system that runs repeatable checks against model behavior for controlled model comparison. 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 Experiment Evaluation Harness when the experiment showed a metric tradeoff, so the team could compare releases with evidence before the model moved into evaluation.”