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 "Metric Hyperparameter Sweep": Metric Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for measurement of model behavior. 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 Metric Hyperparameter Sweep when the metric changed after data cleanup, so the team could find better configurations before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Experiment Calibration Curve": Experiment Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for controlled model comparison. 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.
“예문 초안: The machine learning team used Experiment Calibration Curve when the experiment showed a metric tradeoff, so the team could make confidence scores useful before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Model Drift Hyperparameter Sweep": Model Drift Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for changes in model performance over time. 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 Model Drift Hyperparameter Sweep when the live population changed, so the team could find better configurations before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Model Drift Label Review": Model Drift Label Review is a ml quality workflow that checks annotations for consistency and usefulness for changes in model performance over time. 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.
“예문 초안: The machine learning team used Model Drift Label Review when the live population changed, so the team could improve supervised learning data before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”
기계 지원 번역 초안 (Korean) for "Inference Hyperparameter Sweep": Inference Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for model prediction serving. 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 Inference Hyperparameter Sweep when the endpoint handled burst traffic, so the team could find better configurations before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Label Training Checkpoint": Label Training Checkpoint is a ml recovery artifact that saves model state during learning for ground-truth or weak-supervision annotation. 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.
“예문 초안: The machine learning team used Label Training Checkpoint when the label set had disagreement, so the team could resume or inspect training safely before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”
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 "Metric Bias Audit": Metric Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for measurement of model behavior. 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 Metric Bias Audit when the metric changed after data cleanup, so the team could surface fairness risks before the model moved into evaluation.”