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 "Dataset Feature Store": Dataset Feature Store is a ml service that serves consistent features to training and inference for labeled and unlabeled data used for learning. 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 Dataset Feature Store when the dataset received a new batch, so the team could avoid training-serving skew before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Embedding Provenance Ledger": Embedding Provenance Ledger is a ml record that tracks where data came from and how it changed for vector representation of content or entities. 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 Embedding Provenance Ledger when the embedding index changed, so the team could audit model inputs reliably before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Label Data Split": Label Data Split is a ml experimental control that separates examples for training, validation, and testing for ground-truth or weak-supervision annotation. 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.
“Exemple en brouillon: The machine learning team used Label Data Split when the label set had disagreement, so the team could measure generalization honestly before the model moved into evaluation.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
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 "Training Feature Store": Training Feature Store is a ml service that serves consistent features to training and inference for model learning and optimization workflows. 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 Training Feature Store when the training job restarted, so the team could avoid training-serving skew before the model moved into evaluation.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) for "Label Drift Monitor": Label Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for ground-truth or weak-supervision annotation. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.
“Exemple en brouillon: The machine learning team used Label Drift Monitor when the label set had disagreement, so the team could respond before quality drops before the model moved into evaluation.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”