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 "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 "Experiment Model Card": Experiment Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for controlled model comparison. 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 Experiment Model Card when the experiment showed a metric tradeoff, 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 Hyperparameter Sweep": Inference Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for model prediction serving. 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 Inference Hyperparameter Sweep when the endpoint handled burst traffic, so the team could find better configurations before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Model Drift Bias Audit": Model Drift Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for changes in model performance over time. 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 Model Drift Bias Audit when the live population changed, so the team could surface fairness risks before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Vector Bias Audit": Vector Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for numeric representation and similarity search. 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 Vector Bias Audit when the vector store returned close matches, so the team could surface fairness risks before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) 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.
“示例草稿: 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.”
机器辅助翻译草稿 (Chinese) 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.
“示例草稿: 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.”
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 "Label Provenance Ledger": Label Provenance Ledger is a ml record that tracks where data came from and how it changed for ground-truth or weak-supervision annotation. 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 Label Provenance Ledger when the label set had disagreement, so the team could audit model inputs reliably 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.”