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 "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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Metric Drift Monitor": Metric Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for measurement of model behavior. 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 Metric Drift Monitor when the metric changed after data cleanup, so the team could respond before quality drops before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Label Evaluation Harness": Label Evaluation Harness is a ml test system that runs repeatable checks against model behavior for ground-truth or weak-supervision annotation. 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 Label Evaluation Harness when the label set had disagreement, so the team could compare releases with evidence before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Embedding Calibration Curve": Embedding Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for vector representation of content or entities. 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 Embedding Calibration Curve when the embedding index changed, so the team could make confidence scores useful before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Vector Provenance Ledger": Vector Provenance Ledger is a ml record that tracks where data came from and how it changed for numeric representation and similarity search. 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 Vector Provenance Ledger when the vector store returned close matches, so the team could audit model inputs reliably before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Model Drift Model Card": Model Drift Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for changes in model performance over time. 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.
“Beispielentwurf: The machine learning team used Model Drift Model Card when the live population changed, so the team could publish model behavior honestly before the model moved into evaluation.”