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 "Vector Feature Store": Vector Feature Store is a ml service that serves consistent features to training and inference for numeric representation and similarity search. It uses versioned feature definitions, freshness checks, and access policies so teams can avoid training-serving skew while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Vector Feature Store when the vector store returned close matches, so the team could avoid training-serving skew 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 "Label Model Card": Label Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for ground-truth or weak-supervision annotation. 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 Label Model Card when the label set had disagreement, so the team could publish model behavior honestly before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Model Drift Feature Store": Model Drift Feature Store is a ml service that serves consistent features to training and inference for changes in model performance over time. It uses versioned feature definitions, freshness checks, and access policies so teams can avoid training-serving skew while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Model Drift Feature Store when the live population changed, so the team could avoid training-serving skew before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Model Drift Model Card": Model Drift Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for changes in model performance over time. 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 Model Drift Model Card when the live population 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 "Label Calibration Curve": Label Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for ground-truth or weak-supervision annotation. 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.
“示例草稿: The machine learning team used Label Calibration Curve when the label set had disagreement, so the team could make confidence scores useful before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Label Training Checkpoint": Label Training Checkpoint is a ml recovery artifact that saves model state during learning for ground-truth or weak-supervision annotation. 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 Label Training Checkpoint when the label set had disagreement, 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 "Label Label Review": Label Label Review is a ml quality workflow that checks annotations for consistency and usefulness for ground-truth or weak-supervision annotation. 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 Label Label Review when the label set had disagreement, so the team could improve supervised learning data before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Model Drift Data Split": Model Drift Data Split is a ml experimental control that separates examples for training, validation, and testing for changes in model performance over time. 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 Model Drift Data Split when the live population changed, so the team could measure generalization honestly before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Dataset Bias Audit": Dataset Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for labeled and unlabeled data used for learning. 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 Dataset Bias Audit when the dataset received a new batch, so the team could surface fairness risks before the model moved into evaluation.”