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 "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 "Dataset Embedding Refresh": Dataset Embedding Refresh is a ml index workflow that updates vector representations after source data changes for labeled and unlabeled data used for learning. 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 Dataset Embedding Refresh when the dataset received a new batch, so the team could keep retrieval results current before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Embedding Label Review": Embedding Label Review is a ml quality workflow that checks annotations for consistency and usefulness for vector representation of content or entities. 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 Embedding Label Review when the embedding index changed, so the team could improve supervised learning data before the model moved into evaluation.”
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 "Model Drift Drift Monitor": Model Drift Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for changes in model performance over time. 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 Model Drift Drift Monitor when the live population changed, 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 Embedding Refresh": Experiment Embedding Refresh is a ml index workflow that updates vector representations after source data changes for controlled model comparison. 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 Experiment Embedding Refresh when the experiment showed a metric tradeoff, so the team could keep retrieval results current 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 "Dataset Provenance Ledger": Dataset Provenance Ledger is a ml record that tracks where data came from and how it changed for labeled and unlabeled data used for learning. 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 Dataset Provenance Ledger when the dataset received a new batch, so the team could audit model inputs reliably before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Model Drift Provenance Ledger": Model Drift Provenance Ledger is a ml record that tracks where data came from and how it changed for changes in model performance over time. 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 Model Drift Provenance Ledger when the live population 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 "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.”