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 "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.
“예문 초안: 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.”
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 "Metric Drift Monitor": Metric Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for measurement of model behavior. 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 Metric Drift Monitor when the metric changed after data cleanup, so the team could respond before quality drops before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Training Training Checkpoint": Training Training Checkpoint is a ml recovery artifact that saves model state during learning for model learning and optimization workflows. 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 Training Training Checkpoint when the training job restarted, so the team could resume or inspect training safely 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 "Training Label Review": Training Label Review is a ml quality workflow that checks annotations for consistency and usefulness for model learning and optimization workflows. 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 Training Label Review when the training job restarted, so the team could improve supervised learning data before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Model Drift Data Split": Model Drift Data Split is a ml experimental control that separates examples for training, validation, and testing for changes in model performance over time. 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.
“예문 초안: The machine learning team used Model Drift Data Split when the live population changed, so the team could measure generalization honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Training Data Split": Training Data Split is a ml experimental control that separates examples for training, validation, and testing for model learning and optimization workflows. 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.
“예문 초안: The machine learning team used Training Data Split when the training job restarted, so the team could measure generalization honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Label Label Review": Label Label Review is a ml quality workflow that checks annotations for consistency and usefulness for ground-truth or weak-supervision annotation. 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 Label Label Review when the label set had disagreement, so the team could improve supervised learning data before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”