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
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Model Drift Calibration Curve": Model Drift Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for changes in model performance over time. 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.
“例文の下書き: The machine learning team used Model Drift Calibration Curve when the live population changed, so the team could make confidence scores useful before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Model Drift Evaluation Harness": Model Drift Evaluation Harness is a ml test system that runs repeatable checks against model behavior for changes in model performance over time. 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.
“例文の下書き: The machine learning team used Model Drift Evaluation Harness when the live population changed, so the team could compare releases with evidence before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Inference Bias Audit": Inference Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model prediction serving. 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.
“例文の下書き: The machine learning team used Inference Bias Audit when the endpoint handled burst traffic, so the team could surface fairness risks before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Inference Model Card": Inference Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for model prediction serving. 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.
“例文の下書き: The machine learning team used Inference Model Card when the endpoint handled burst traffic, so the team could publish model behavior honestly before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Dataset Label Review": Dataset Label Review is a ml quality workflow that checks annotations for consistency and usefulness for labeled and unlabeled data used for learning. 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.
“例文の下書き: The machine learning team used Dataset Label Review when the dataset received a new batch, so the team could improve supervised learning data before the model moved into evaluation.”