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
مسودة ترجمة بمساعدة آلية (Arabic) for "Metric Bias Audit": Metric Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for measurement of model behavior. 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.
“مسودة مثال: The machine learning team used Metric Bias Audit when the metric changed after data cleanup, so the team could surface fairness risks before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Training Data Split": Training Data Split is a ml experimental control that separates examples for training, validation, and testing for model learning and optimization workflows. 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.
“مسودة مثال: The machine learning team used Training Data Split when the training job restarted, so the team could measure generalization honestly before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Vector Embedding Refresh": Vector Embedding Refresh is a ml index workflow that updates vector representations after source data changes for numeric representation and similarity search. 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.
“مسودة مثال: The machine learning team used Vector Embedding Refresh when the vector store returned close matches, so the team could keep retrieval results current before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Experiment Evaluation Harness": Experiment Evaluation Harness is a ml test system that runs repeatable checks against model behavior for controlled model comparison. 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.
“مسودة مثال: The machine learning team used Experiment Evaluation Harness when the experiment showed a metric tradeoff, so the team could compare releases with evidence before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Metric Hyperparameter Sweep": Metric Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for measurement of model behavior. 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.
“مسودة مثال: The machine learning team used Metric Hyperparameter Sweep when the metric changed after data cleanup, so the team could find better configurations before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Model Drift Hyperparameter Sweep": Model Drift Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for changes in model performance over time. 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.
“مسودة مثال: The machine learning team used Model Drift Hyperparameter Sweep when the live population changed, so the team could find better configurations before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Label Label Review": Label Label Review is a ml quality workflow that checks annotations for consistency and usefulness for ground-truth or weak-supervision annotation. 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.
“مسودة مثال: The machine learning team used Label Label Review when the label set had disagreement, so the team could improve supervised learning data before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Inference Hyperparameter Sweep": Inference Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for model prediction serving. 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.
“مسودة مثال: The machine learning team used Inference Hyperparameter Sweep when the endpoint handled burst traffic, so the team could find better configurations before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) for "Training Embedding Refresh": Training Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model learning and optimization workflows. 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.
“مسودة مثال: The machine learning team used Training Embedding Refresh when the training job restarted, so the team could keep retrieval results current before the model moved into evaluation.”
مسودة ترجمة بمساعدة آلية (Arabic) 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.
“مسودة مثال: 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.”