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 "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 "Experiment Embedding Refresh": Experiment Embedding Refresh is a ml index workflow that updates vector representations after source data changes for controlled model comparison. 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 Experiment Embedding Refresh when the experiment showed a metric tradeoff, so the team could keep retrieval results current before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Inference Provenance Ledger": Inference Provenance Ledger is a ml record that tracks where data came from and how it changed for model prediction serving. 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 Inference Provenance Ledger when the endpoint handled burst traffic, so the team could audit model inputs reliably before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Label Drift Monitor": Label Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for ground-truth or weak-supervision annotation. 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 Label Drift Monitor when the label set had disagreement, so the team could respond before quality drops before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Embedding Embedding Refresh": Embedding Embedding Refresh is a ml index workflow that updates vector representations after source data changes for vector representation of content or entities. 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 Embedding Embedding Refresh when the embedding index changed, so the team could keep retrieval results current before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Inference Feature Store": Inference Feature Store is a ml service that serves consistent features to training and inference for model prediction serving. 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 Inference Feature Store when the endpoint handled burst traffic, 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 "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 "Inference Embedding Refresh": Inference Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model prediction serving. 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 Inference Embedding Refresh when the endpoint handled burst traffic, so the team could keep retrieval results current before the model moved into evaluation.”
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.”