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 "Dataset Embedding Refresh": Dataset Embedding Refresh is a ml index workflow that updates vector representations after source data changes for labeled and unlabeled data used for learning. 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 Dataset Embedding Refresh when the dataset received a new batch, so the team could keep retrieval results current before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Label Evaluation Harness": Label Evaluation Harness is a ml test system that runs repeatable checks against model behavior for ground-truth or weak-supervision annotation. 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 Label Evaluation Harness when the label set had disagreement, so the team could compare releases with evidence before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Embedding Calibration Curve": Embedding Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for vector representation of content or entities. 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 Embedding Calibration Curve when the embedding index changed, so the team could make confidence scores useful 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 "Dataset Model Card": Dataset Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for labeled and unlabeled data used for learning. 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 Dataset Model Card when the dataset received a new batch, so the team could publish model behavior honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Vector Data Split": Vector Data Split is a ml experimental control that separates examples for training, validation, and testing for numeric representation and similarity search. 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 Vector Data Split when the vector store returned close matches, so the team could measure generalization honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Model Drift Training Checkpoint": Model Drift Training Checkpoint is a ml recovery artifact that saves model state during learning for changes in model performance over time. 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 Model Drift Training Checkpoint when the live population changed, so the team could resume or inspect training safely 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 "Inference Label Review": Inference Label Review is a ml quality workflow that checks annotations for consistency and usefulness for model prediction serving. 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 Inference Label Review when the endpoint handled burst traffic, so the team could improve supervised learning data before the model moved into evaluation.”