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 "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 "Metric Provenance Ledger": Metric Provenance Ledger is a ml record that tracks where data came from and how it changed for measurement of model behavior. 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 Metric Provenance Ledger when the metric changed after data cleanup, so the team could audit model inputs reliably before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Inference Data Split": Inference Data Split is a ml experimental control that separates examples for training, validation, and testing for model prediction serving. 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 Inference Data Split when the endpoint handled burst traffic, so the team could measure generalization honestly 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 "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 "Label Embedding Refresh": Label Embedding Refresh is a ml index workflow that updates vector representations after source data changes for ground-truth or weak-supervision annotation. 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 Label Embedding Refresh when the label set had disagreement, so the team could keep retrieval results current 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 "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 "Training Calibration Curve": Training Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for model learning and optimization workflows. 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 Training Calibration Curve when the training job restarted, so the team could make confidence scores useful before the model moved into evaluation.”
Rascunho de traducao automatica (Portuguese) for "Vector Drift Monitor": Vector Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for numeric representation and similarity search. 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 Vector Drift Monitor when the vector store returned close matches, so the team could respond before quality drops before the model moved into evaluation.”