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 "Dataset Label Review": Dataset Label Review is a ml quality workflow that checks annotations for consistency and usefulness for labeled and unlabeled data used for learning. 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 Dataset Label Review when the dataset received a new batch, so the team could improve supervised learning data before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Embedding Bias Audit": Embedding Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for vector representation of content or entities. 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 Embedding Bias Audit when the embedding index changed, so the team could surface fairness risks before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Embedding Calibration Curve": Embedding Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for vector representation of content or entities. 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 Embedding Calibration Curve when the embedding index changed, so the team could make confidence scores useful 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 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 "Inference Drift Monitor": Inference Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for model prediction serving. 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.
“Exemplo em rascunho: The machine learning team used Inference Drift Monitor when the endpoint handled burst traffic, so the team could respond before quality drops before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Vector Hyperparameter Sweep": Vector Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for numeric representation and similarity search. 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 Vector Hyperparameter Sweep when the vector store returned close matches, so the team could find better configurations before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) for "Vector Evaluation Harness": Vector Evaluation Harness is a ml test system that runs repeatable checks against model behavior for numeric representation and similarity search. 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 Vector Evaluation Harness when the vector store returned close matches, so the team could compare releases with evidence before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Label Calibration Curve": Label Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for ground-truth or weak-supervision annotation. 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 Label Calibration Curve when the label set had disagreement, so the team could make confidence scores useful before the model moved into evaluation.”