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 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 "Experiment Drift Monitor": Experiment Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for controlled model comparison. 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 Experiment Drift Monitor when the experiment showed a metric tradeoff, so the team could respond before quality drops before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) 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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) for "Metric Evaluation Harness": Metric Evaluation Harness is a ml test system that runs repeatable checks against model behavior for measurement of model behavior. 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 Metric Evaluation Harness when the metric changed after data cleanup, so the team could compare releases with evidence before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Metric Provenance Ledger": Metric Provenance Ledger is a ml record that tracks where data came from and how it changed for measurement of model behavior. 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 Metric Provenance Ledger when the metric changed after data cleanup, so the team could audit model inputs reliably before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Inference Drift Monitor": Inference Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for model prediction serving. 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 Inference Drift Monitor when the endpoint handled burst traffic, so the team could respond before quality drops before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Training Embedding Refresh": Training Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model learning and optimization workflows. 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 Training Embedding Refresh when the training job restarted, so the team could keep retrieval results current before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Label Hyperparameter Sweep": Label Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for ground-truth or weak-supervision annotation. 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.
“示例草稿: The machine learning team used Label Hyperparameter Sweep when the label set had disagreement, so the team could find better configurations 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 "Vector Training Checkpoint": Vector Training Checkpoint is a ml recovery artifact that saves model state during learning for numeric representation and similarity search. 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 Vector Training Checkpoint when the vector store returned close matches, so the team could resume or inspect training safely before the model moved into evaluation.”