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 "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.
“示例草稿: 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.”
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 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 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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) 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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) for "Vector Drift Monitor": Vector Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for numeric representation and similarity search. 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 Vector Drift Monitor when the vector store returned close matches, so the team could respond before quality drops before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Experiment Evaluation Harness": Experiment Evaluation Harness is a ml test system that runs repeatable checks against model behavior for controlled model comparison. 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 Experiment Evaluation Harness when the experiment showed a metric tradeoff, so the team could compare releases with evidence 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 "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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) for "Label Embedding Refresh": Label Embedding Refresh is a ml index workflow that updates vector representations after source data changes for ground-truth or weak-supervision annotation. 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.
“示例草稿: The machine learning team used Label Embedding Refresh when the label set had disagreement, so the team could keep retrieval results current before the model moved into evaluation.”