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 "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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) for "Metric Hyperparameter Sweep": Metric Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for measurement of model behavior. 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 Metric Hyperparameter Sweep when the metric changed after data cleanup, so the team could find better configurations before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Model Drift Drift Monitor": Model Drift Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for changes in model performance over time. 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 Model Drift Drift Monitor when the live population changed, so the team could respond before quality drops before the model moved into evaluation.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
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.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
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.”