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 "Vector Training Checkpoint": Vector Training Checkpoint is a ml recovery artifact that saves model state during learning for numeric representation and similarity search. 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 Vector Training Checkpoint when the vector store returned close matches, so the team could resume or inspect training safely 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 "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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Inference Feature Store": Inference Feature Store is a ml service that serves consistent features to training and inference for model prediction serving. 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 Inference Feature Store when the endpoint handled burst traffic, so the team could avoid training-serving skew before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Inference Embedding Refresh": Inference Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model prediction serving. 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 Inference Embedding Refresh when the endpoint handled burst traffic, so the team could keep retrieval results current before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Model Drift Feature Store": Model Drift Feature Store is a ml service that serves consistent features to training and inference for changes in model performance over time. 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 Model Drift Feature Store when the live population changed, so the team could avoid training-serving skew before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Label Label Review": Label Label Review is a ml quality workflow that checks annotations for consistency and usefulness for ground-truth or weak-supervision annotation. 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 Label Label Review when the label set had disagreement, so the team could improve supervised learning data before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”