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 "Experiment Calibration Curve": Experiment Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for controlled model comparison. 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 Experiment Calibration Curve when the experiment showed a metric tradeoff, 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 "Metric Provenance Ledger": Metric Provenance Ledger is a ml record that tracks where data came from and how it changed for measurement of model behavior. 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 Metric Provenance Ledger when the metric changed after data cleanup, 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 "Model Drift Data Split": Model Drift Data Split is a ml experimental control that separates examples for training, validation, and testing for changes in model performance over time. 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 Model Drift Data Split when the live population changed, 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 "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.
“例文の下書き: 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.”
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 "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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Label Calibration Curve": Label Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for ground-truth or weak-supervision annotation. 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 Label Calibration Curve when the label set had disagreement, 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 "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 "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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”