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 "Inference Calibration Curve": Inference Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for model prediction serving. 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 Inference Calibration Curve when the endpoint handled burst traffic, so the team could make confidence scores useful before the model moved into evaluation.”
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 "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.
“Exemplo em rascunho: 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.”
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 "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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) for "Model Drift Data Split": Model Drift Data Split is a ml experimental control that separates examples for training, validation, and testing for changes in model performance over time. 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 Model Drift Data Split when the live population changed, so the team could measure generalization honestly before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Model Drift Provenance Ledger": Model Drift Provenance Ledger is a ml record that tracks where data came from and how it changed for changes in model performance over time. 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 Model Drift Provenance Ledger when the live population changed, so the team could audit model inputs reliably before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Label Bias Audit": Label Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for ground-truth or weak-supervision annotation. 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.
“Exemplo em rascunho: The machine learning team used Label Bias Audit when the label set had disagreement, so the team could surface fairness risks 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 "Embedding Data Split": Embedding Data Split is a ml experimental control that separates examples for training, validation, and testing for vector representation of content or entities. 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 Embedding Data Split when the embedding index changed, so the team could measure generalization honestly before the model moved into evaluation.”