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 "Inference Training Checkpoint": Inference Training Checkpoint is a ml recovery artifact that saves model state during learning for model prediction serving. 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 Inference Training Checkpoint when the endpoint handled burst traffic, so the team could resume or inspect training safely before the model moved into evaluation.”
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 "Inference Label Review": Inference Label Review is a ml quality workflow that checks annotations for consistency and usefulness for model prediction serving. 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 Inference Label Review when the endpoint handled burst traffic, so the team could improve supervised learning data before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Model Drift Evaluation Harness": Model Drift Evaluation Harness is a ml test system that runs repeatable checks against model behavior for changes in model performance over time. 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 Model Drift Evaluation Harness when the live population changed, so the team could compare releases with evidence 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 "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 "Training Hyperparameter Sweep": Training Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for model learning and optimization workflows. 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 Training Hyperparameter Sweep when the training job restarted, so the team could find better configurations before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Dataset Training Checkpoint": Dataset Training Checkpoint is a ml recovery artifact that saves model state during learning for labeled and unlabeled data used for learning. 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 Dataset Training Checkpoint when the dataset received a new batch, so the team could resume or inspect training safely before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Training Training Checkpoint": Training Training Checkpoint is a ml recovery artifact that saves model state during learning for model learning and optimization workflows. 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 Training Training Checkpoint when the training job restarted, so the team could resume or inspect training safely before the model moved into evaluation.”
Brouillon de traduction automatique (French) for "Dataset Model Card": Dataset Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for labeled and unlabeled data used for learning. 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 Dataset Model Card when the dataset received a new batch, so the team could publish model behavior honestly before the model moved into evaluation.”