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
기계 지원 번역 초안 (Korean) for "Vector Model Card": Vector Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for numeric representation and similarity search. 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.
“예문 초안: The machine learning team used Vector Model Card when the vector store returned close matches, so the team could publish model behavior honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Training Model Card": Training Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for model learning and optimization workflows. 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.
“예문 초안: The machine learning team used Training Model Card when the training job restarted, so the team could publish model behavior honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Training Embedding Refresh": Training Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model learning and optimization workflows. 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.
“예문 초안: The machine learning team used Training Embedding Refresh when the training job restarted, so the team could keep retrieval results current before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Model Drift Bias Audit": Model Drift Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for changes in model performance over time. 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.
“예문 초안: The machine learning team used Model Drift Bias Audit when the live population changed, so the team could surface fairness risks before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”
기계 지원 번역 초안 (Korean) for "Label Provenance Ledger": Label Provenance Ledger is a ml record that tracks where data came from and how it changed for ground-truth or weak-supervision annotation. 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.
“예문 초안: The machine learning team used Label Provenance Ledger when the label set had disagreement, so the team could audit model inputs reliably before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Vector Embedding Refresh": Vector Embedding Refresh is a ml index workflow that updates vector representations after source data changes for numeric representation and similarity search. 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.
“예문 초안: The machine learning team used Vector Embedding Refresh when the vector store returned close matches, so the team could keep retrieval results current before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”