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
Automatischer Uebersetzungsentwurf (German) for "Label Provenance Ledger": Label Provenance Ledger is a ml record that tracks where data came from and how it changed for ground-truth or weak-supervision annotation. 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.
“Beispielentwurf: The machine learning team used Label Provenance Ledger when the label set had disagreement, so the team could audit model inputs reliably before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Training Evaluation Harness": Training Evaluation Harness is a ml test system that runs repeatable checks against model behavior for model learning and optimization workflows. 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.
“Beispielentwurf: The machine learning team used Training Evaluation Harness when the training job restarted, so the team could compare releases with evidence before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Vector Drift Monitor": Vector Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for numeric representation and similarity search. 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.
“Beispielentwurf: The machine learning team used Vector Drift Monitor when the vector store returned close matches, so the team could respond before quality drops before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Dataset Bias Audit": Dataset Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for labeled and unlabeled data used for learning. 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.
“Beispielentwurf: The machine learning team used Dataset Bias Audit when the dataset received a new batch, so the team could surface fairness risks before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Dataset Embedding Refresh": Dataset Embedding Refresh is a ml index workflow that updates vector representations after source data changes for labeled and unlabeled data used for learning. 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.
“Beispielentwurf: The machine learning team used Dataset Embedding Refresh when the dataset received a new batch, so the team could keep retrieval results current before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Embedding Data Split": Embedding Data Split is a ml experimental control that separates examples for training, validation, and testing for vector representation of content or entities. 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.
“Beispielentwurf: The machine learning team used Embedding Data Split when the embedding index changed, so the team could measure generalization honestly before the model moved into evaluation.”