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 "Training Hyperparameter Sweep": Training Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for model learning and optimization workflows. 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 Training Hyperparameter Sweep when the training job restarted, so the team could find better configurations before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Dataset Hyperparameter Sweep": Dataset Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for labeled and unlabeled data used for learning. 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 Dataset Hyperparameter Sweep when the dataset received a new batch, so the team could find better configurations before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Model Drift Embedding Refresh": Model Drift Embedding Refresh is a ml index workflow that updates vector representations after source data changes for changes in model performance over time. 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 Model Drift Embedding Refresh when the live population changed, so the team could keep retrieval results current before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Embedding Evaluation Harness": Embedding Evaluation Harness is a ml test system that runs repeatable checks against model behavior for vector representation of content or entities. It uses fixtures, metrics, thresholds, and regression reports so teams can compare releases with evidence while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The machine learning team used Embedding Evaluation Harness when the embedding index changed, so the team could compare releases with evidence before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Experiment Feature Store": Experiment Feature Store is a ml service that serves consistent features to training and inference for controlled model comparison. 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 Experiment Feature Store when the experiment showed a metric tradeoff, so the team could avoid training-serving skew before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Experiment Training Checkpoint": Experiment Training Checkpoint is a ml recovery artifact that saves model state during learning for controlled model comparison. 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 Experiment Training Checkpoint when the experiment showed a metric tradeoff, so the team could resume or inspect training safely before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Dataset Drift Monitor": Dataset Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for labeled and unlabeled data used for learning. 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 Dataset Drift Monitor when the dataset received a new batch, so the team could respond before quality drops before the model moved into evaluation.”
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 "Dataset Training Checkpoint": Dataset Training Checkpoint is a ml recovery artifact that saves model state during learning for labeled and unlabeled data used for learning. 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 Dataset Training Checkpoint when the dataset received a new batch, so the team could resume or inspect training safely 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.”