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 Embedding Refresh": Dataset Embedding Refresh is a ml index workflow that updates vector representations after source data changes for labeled and unlabeled data used for learning. 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 Dataset Embedding Refresh when the dataset received a new batch, so the team could keep retrieval results current before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Inference Bias Audit": Inference Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model prediction serving. 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 Inference Bias Audit when the endpoint handled burst traffic, so the team could surface fairness risks 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 "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.”
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 "Embedding Hyperparameter Sweep": Embedding Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for vector representation of content or entities. 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 Embedding Hyperparameter Sweep when the embedding index changed, so the team could find better configurations before the model moved into evaluation.”
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 "Vector Calibration Curve": Vector Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for numeric representation and similarity search. 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 Vector Calibration Curve when the vector store returned close matches, so the team could make confidence scores useful before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Vector Provenance Ledger": Vector Provenance Ledger is a ml record that tracks where data came from and how it changed for numeric representation and similarity search. 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 Vector Provenance Ledger when the vector store returned close matches, so the team could audit model inputs reliably 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.”