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
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Label Hyperparameter Sweep": Label Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for ground-truth or weak-supervision annotation. 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.
“Ejemplo en borrador: The machine learning team used Label Hyperparameter Sweep when the label set had disagreement, so the team could find better configurations before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Model Drift Data Split": Model Drift Data Split is a ml experimental control that separates examples for training, validation, and testing for changes in model performance over time. 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.
“Ejemplo en borrador: The machine learning team used Model Drift Data Split when the live population changed, so the team could measure generalization honestly before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Vector Hyperparameter Sweep": Vector Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for numeric representation and similarity search. 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.
“Ejemplo en borrador: The machine learning team used Vector Hyperparameter Sweep when the vector store returned close matches, so the team could find better configurations before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Experiment Embedding Refresh": Experiment Embedding Refresh is a ml index workflow that updates vector representations after source data changes for controlled model comparison. 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.
“Ejemplo en borrador: The machine learning team used Experiment Embedding Refresh when the experiment showed a metric tradeoff, so the team could keep retrieval results current before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Training Calibration Curve": Training Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for model learning and optimization workflows. 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.
“Ejemplo en borrador: The machine learning team used Training Calibration Curve when the training job restarted, so the team could make confidence scores useful before the model moved into evaluation.”