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 Hyperparameter Sweep": Embedding Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for vector representation of content or entities. 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 Embedding Hyperparameter Sweep when the embedding index changed, so the team could find better configurations before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Embedding Data Split": Embedding Data Split is a ml experimental control that separates examples for training, validation, and testing for vector representation of content or entities. 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 Embedding Data Split when the embedding index changed, so the team could measure generalization honestly before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Experiment Data Split": Experiment Data Split is a ml experimental control that separates examples for training, validation, and testing for controlled model comparison. 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 Experiment Data Split when the experiment showed a metric tradeoff, so the team could measure generalization honestly before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Model Drift Provenance Ledger": Model Drift Provenance Ledger is a ml record that tracks where data came from and how it changed for changes in model performance over time. 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 Model Drift Provenance Ledger when the live population changed, so the team could audit model inputs reliably 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 "Embedding Calibration Curve": Embedding Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for vector representation of content or entities. 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 Embedding Calibration Curve when the embedding index changed, so the team could make confidence scores useful before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Model Drift Embedding Refresh": Model Drift Embedding Refresh is a ml index workflow that updates vector representations after source data changes for changes in model performance over time. 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 Model Drift Embedding Refresh when the live population changed, so the team could keep retrieval results current 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 "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 "Experiment Hyperparameter Sweep": Experiment Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for controlled model comparison. 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 Experiment Hyperparameter Sweep when the experiment showed a metric tradeoff, so the team could find better configurations before the model moved into evaluation.”