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
Rascunho de traducao automatica (Portuguese) for "Label Feature Store": Label Feature Store is a ml service that serves consistent features to training and inference for ground-truth or weak-supervision annotation. 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.
“Exemplo em rascunho: The machine learning team used Label Feature Store when the label set had disagreement, so the team could avoid training-serving skew before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) for "Embedding Label Review": Embedding Label Review is a ml quality workflow that checks annotations for consistency and usefulness for vector representation of content or entities. 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.
“Exemplo em rascunho: The machine learning team used Embedding Label Review when the embedding index changed, so the team could improve supervised learning data before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”