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 "Embedding Evaluation Harness": Embedding Evaluation Harness is a ml test system that runs repeatable checks against model behavior for vector representation of content or entities. 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 Embedding Evaluation Harness when the embedding index changed, so the team could compare releases with evidence before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Metric Evaluation Harness": Metric Evaluation Harness is a ml test system that runs repeatable checks against model behavior for measurement of model behavior. 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 Metric Evaluation Harness when the metric changed after data cleanup, so the team could compare releases with evidence before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Model Drift Model Card": Model Drift Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for changes in model performance over time. 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.
“Exemplo em rascunho: The machine learning team used Model Drift Model Card when the live population changed, so the team could publish model behavior honestly before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Label Model Card": Label Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for ground-truth or weak-supervision annotation. 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.
“Exemplo em rascunho: The machine learning team used Label Model Card when the label set had disagreement, so the team could publish model behavior honestly before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Label Training Checkpoint": Label Training Checkpoint is a ml recovery artifact that saves model state during learning for ground-truth or weak-supervision annotation. 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 Label Training Checkpoint when the label set had disagreement, so the team could resume or inspect training safely before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Label Provenance Ledger": Label Provenance Ledger is a ml record that tracks where data came from and how it changed for ground-truth or weak-supervision annotation. 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.
“Exemplo em rascunho: The machine learning team used Label Provenance Ledger when the label set had disagreement, so the team could audit model inputs reliably before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Experiment Model Card": Experiment Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for controlled model comparison. 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.
“Exemplo em rascunho: The machine learning team used Experiment Model Card when the experiment showed a metric tradeoff, so the team could publish model behavior honestly before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”