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
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) for "Label Provenance Ledger": Label Provenance Ledger is a ml record that tracks where data came from and how it changed for ground-truth or weak-supervision annotation. 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.
“Exemple en brouillon: The machine learning team used Label Provenance Ledger when the label set had disagreement, so the team could audit model inputs reliably before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Label Embedding Refresh": Label Embedding Refresh is a ml index workflow that updates vector representations after source data changes for ground-truth or weak-supervision annotation. 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.
“Exemple en brouillon: The machine learning team used Label Embedding Refresh when the label set had disagreement, so the team could keep retrieval results current before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Embedding Model Card": Embedding Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for vector representation of content or entities. 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.
“Exemple en brouillon: The machine learning team used Embedding Model Card when the embedding index changed, so the team could publish model behavior honestly before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Embedding Bias Audit": Embedding Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for vector representation of content or entities. 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.
“Exemple en brouillon: The machine learning team used Embedding Bias Audit when the embedding index changed, so the team could surface fairness risks before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Training Model Card": Training Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for model learning and optimization workflows. 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.
“Exemple en brouillon: The machine learning team used Training Model Card when the training job restarted, so the team could publish model behavior honestly before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Experiment Feature Store": Experiment Feature Store is a ml service that serves consistent features to training and inference for controlled model comparison. 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.
“Exemple en brouillon: The machine learning team used Experiment Feature Store when the experiment showed a metric tradeoff, so the team could avoid training-serving skew before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Training Embedding Refresh": Training Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model learning and optimization workflows. 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.
“Exemple en brouillon: The machine learning team used Training Embedding Refresh when the training job restarted, so the team could keep retrieval results current before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Vector Label Review": Vector Label Review is a ml quality workflow that checks annotations for consistency and usefulness for numeric representation and similarity search. 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.
“Exemple en brouillon: The machine learning team used Vector Label Review when the vector store returned close matches, so the team could improve supervised learning data before the model moved into evaluation.”