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 "Training Model Card": Training Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for model learning and optimization workflows. 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 Training Model Card when the training job restarted, 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 "Experiment Training Checkpoint": Experiment Training Checkpoint is a ml recovery artifact that saves model state during learning for controlled model comparison. 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.
“例文の下書き: The machine learning team used Experiment Training Checkpoint when the experiment showed a metric tradeoff, so the team could resume or inspect training safely before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Embedding Drift Monitor": Embedding Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for vector representation of content or entities. 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.
“例文の下書き: The machine learning team used Embedding Drift Monitor when the embedding index changed, so the team could respond before quality drops before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Vector Evaluation Harness": Vector Evaluation Harness is a ml test system that runs repeatable checks against model behavior for numeric representation and similarity search. 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 Vector Evaluation Harness when the vector store returned close matches, 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 "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 "Dataset Data Split": Dataset Data Split is a ml experimental control that separates examples for training, validation, and testing for labeled and unlabeled data used for learning. 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.
“例文の下書き: The machine learning team used Dataset Data Split when the dataset received a new batch, so the team could measure generalization honestly before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
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 "Dataset Feature Store": Dataset Feature Store is a ml service that serves consistent features to training and inference for labeled and unlabeled data used for learning. 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 Dataset Feature Store when the dataset received a new batch, so the team could avoid training-serving skew before the model moved into evaluation.”