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 Evaluation Harness": Embedding Evaluation Harness is a ml test system that runs repeatable checks against model behavior for vector representation of content or entities. 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 Embedding Evaluation Harness when the embedding index 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 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 "Embedding Provenance Ledger": Embedding Provenance Ledger is a ml record that tracks where data came from and how it changed for vector representation of content or entities. 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 Embedding Provenance Ledger when the embedding index changed, 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 "Vector Calibration Curve": Vector Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for numeric representation and similarity search. 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 Vector Calibration Curve when the vector store returned close matches, 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 "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 "Vector Data Split": Vector Data Split is a ml experimental control that separates examples for training, validation, and testing for numeric representation and similarity search. 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 Vector Data Split when the vector store returned close matches, 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 "Experiment Provenance Ledger": Experiment Provenance Ledger is a ml record that tracks where data came from and how it changed for controlled model comparison. 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 Experiment Provenance Ledger when the experiment showed a metric tradeoff, 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 "Experiment Hyperparameter Sweep": Experiment Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for controlled model comparison. 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 Experiment Hyperparameter Sweep when the experiment showed a metric tradeoff, 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 "Dataset Drift Monitor": Dataset Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for labeled and unlabeled data used for learning. 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 Dataset Drift Monitor when the dataset received a new batch, 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 "Dataset Training Checkpoint": Dataset Training Checkpoint is a ml recovery artifact that saves model state during learning for labeled and unlabeled data used for learning. 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 Dataset Training Checkpoint when the dataset received a new batch, so the team could resume or inspect training safely before the model moved into evaluation.”