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 "Label Training Checkpoint": Label Training Checkpoint is a ml recovery artifact that saves model state during learning for ground-truth or weak-supervision annotation. 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 Label Training Checkpoint when the label set had disagreement, so the team could resume or inspect training safely before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Embedding Label Review": Embedding Label Review is a ml quality workflow that checks annotations for consistency and usefulness for vector representation of content or entities. 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 Embedding Label Review when the embedding index changed, so the team could improve supervised learning data before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Vector Model Card": Vector Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for numeric representation and similarity search. 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 Vector Model Card when the vector store returned close matches, so the team could publish model behavior honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Vector Embedding Refresh": Vector Embedding Refresh is a ml index workflow that updates vector representations after source data changes for numeric representation and similarity search. 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 Vector Embedding Refresh when the vector store returned close matches, so the team could keep retrieval results current before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Metric Feature Store": Metric Feature Store is a ml service that serves consistent features to training and inference for measurement of model behavior. 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 Metric Feature Store when the metric changed after data cleanup, so the team could avoid training-serving skew before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Vector Calibration Curve": Vector Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for numeric representation and similarity search. 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 Vector Calibration Curve when the vector store returned close matches, so the team could make confidence scores useful 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 "Experiment Calibration Curve": Experiment Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for controlled model comparison. 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 Experiment Calibration Curve when the experiment showed a metric tradeoff, so the team could make confidence scores useful before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Embedding Evaluation Harness": Embedding Evaluation Harness is a ml test system that runs repeatable checks against model behavior for vector representation of content or entities. 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 Embedding Evaluation Harness when the embedding index changed, so the team could compare releases with evidence before the model moved into evaluation.”
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.”