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
机器辅助翻译草稿 (Chinese) 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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) for "Inference Data Split": Inference Data Split is a ml experimental control that separates examples for training, validation, and testing for model prediction serving. 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.
“示例草稿: The machine learning team used Inference Data Split when the endpoint handled burst traffic, so the team could measure generalization honestly before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) 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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) for "Embedding Provenance Ledger": Embedding Provenance Ledger is a ml record that tracks where data came from and how it changed for vector representation of content or entities. 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.
“示例草稿: The machine learning team used Embedding Provenance Ledger when the embedding index changed, so the team could audit model inputs reliably before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Training Bias Audit": Training Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model learning and optimization workflows. 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.
“示例草稿: The machine learning team used Training Bias Audit when the training job restarted, so the team could surface fairness risks before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Inference Provenance Ledger": Inference Provenance Ledger is a ml record that tracks where data came from and how it changed for model prediction serving. 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.
“示例草稿: The machine learning team used Inference Provenance Ledger when the endpoint handled burst traffic, so the team could audit model inputs reliably before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) 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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) for "Inference Bias Audit": Inference Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model prediction serving. 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.
“示例草稿: The machine learning team used Inference Bias Audit when the endpoint handled burst traffic, so the team could surface fairness risks before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Embedding Model Card": Embedding Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for vector representation of content or entities. 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.
“示例草稿: The machine learning team used Embedding Model Card when the embedding index changed, so the team could publish model behavior honestly before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Dataset Data Split": Dataset Data Split is a ml experimental control that separates examples for training, validation, and testing for labeled and unlabeled data used for learning. 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.
“示例草稿: The machine learning team used Dataset Data Split when the dataset received a new batch, so the team could measure generalization honestly before the model moved into evaluation.”