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 "Embedding Training Checkpoint": Embedding Training Checkpoint is a ml recovery artifact that saves model state during learning for vector representation of content or entities. 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 Embedding Training Checkpoint when the embedding index changed, 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 "Experiment Feature Store": Experiment Feature Store is a ml service that serves consistent features to training and inference for controlled model comparison. 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 Experiment Feature Store when the experiment showed a metric tradeoff, 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 "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 "Experiment Label Review": Experiment Label Review is a ml quality workflow that checks annotations for consistency and usefulness for controlled model comparison. 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 Experiment Label Review when the experiment showed a metric tradeoff, 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 "Dataset Hyperparameter Sweep": Dataset Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for labeled and unlabeled data used for learning. 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.
“例文の下書き: The machine learning team used Dataset Hyperparameter Sweep when the dataset received a new batch, so the team could find better configurations before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Dataset Model Card": Dataset Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for labeled and unlabeled data used for learning. 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 Dataset Model Card when the dataset received a new batch, 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 "Training Training Checkpoint": Training Training Checkpoint is a ml recovery artifact that saves model state during learning for model learning and optimization workflows. 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 Training Training Checkpoint when the training job restarted, 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 "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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Inference Training Checkpoint": Inference Training Checkpoint is a ml recovery artifact that saves model state during learning for model prediction serving. 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 Inference Training Checkpoint when the endpoint handled burst traffic, so the team could resume or inspect training safely before the model moved into evaluation.”