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
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Inference Data Split": Inference Data Split is a ml experimental control that separates examples for training, validation, and testing for model prediction serving. 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.
“Ejemplo en borrador: The machine learning team used Inference Data Split when the endpoint handled burst traffic, so the team could measure generalization honestly before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Embedding Drift Monitor": Embedding Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for vector representation of content or entities. 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.
“Ejemplo en borrador: The machine learning team used Embedding Drift Monitor when the embedding index changed, so the team could respond before quality drops before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Model Drift Label Review": Model Drift Label Review is a ml quality workflow that checks annotations for consistency and usefulness for changes in model performance over time. 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.
“Ejemplo en borrador: The machine learning team used Model Drift Label Review when the live population changed, so the team could improve supervised learning data before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Training Bias Audit": Training Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model learning and optimization workflows. 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.
“Ejemplo en borrador: The machine learning team used Training Bias Audit when the training job restarted, so the team could surface fairness risks before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Training Embedding Refresh": Training Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model learning and optimization workflows. 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.
“Ejemplo en borrador: The machine learning team used Training Embedding Refresh when the training job restarted, so the team could keep retrieval results current before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”