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 "Experiment Provenance Ledger": Experiment Provenance Ledger is a ml record that tracks where data came from and how it changed for controlled model comparison. 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 Experiment Provenance Ledger when the experiment showed a metric tradeoff, so the team could audit model inputs reliably before the model moved into evaluation.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) 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.
“Exemple en brouillon: 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.”
Brouillon de traduction automatique (French) for "Embedding Training Checkpoint": Embedding Training Checkpoint is a ml recovery artifact that saves model state during learning for vector representation of content or entities. 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.
“Exemple en brouillon: The machine learning team used Embedding Training Checkpoint when the embedding index changed, so the team could resume or inspect training safely before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Inference Evaluation Harness": Inference Evaluation Harness is a ml test system that runs repeatable checks against model behavior for model prediction serving. 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 Inference Evaluation Harness when the endpoint handled burst traffic, so the team could compare releases with evidence before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Vector Data Split": Vector Data Split is a ml experimental control that separates examples for training, validation, and testing for numeric representation and similarity search. 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 Vector Data Split when the vector store returned close matches, so the team could measure generalization honestly before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Metric Drift Monitor": Metric Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for measurement of model behavior. 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 Metric Drift Monitor when the metric changed after data cleanup, so the team could respond before quality drops before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Model Drift Bias Audit": Model Drift Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for changes in model performance over time. 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 Model Drift Bias Audit when the live population changed, so the team could surface fairness risks 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 "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.”