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
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Experiment Data Split": Experiment Data Split is a ml experimental control that separates examples for training, validation, and testing for controlled model comparison. 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.
“उदाहरण मसौदा: The machine learning team used Experiment Data Split when the experiment showed a metric tradeoff, so the team could measure generalization honestly before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Label Drift Monitor": Label Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for ground-truth or weak-supervision annotation. 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.
“उदाहरण मसौदा: The machine learning team used Label Drift Monitor when the label set had disagreement, so the team could respond before quality drops before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Inference Label Review": Inference Label Review is a ml quality workflow that checks annotations for consistency and usefulness for model prediction serving. 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.
“उदाहरण मसौदा: The machine learning team used Inference Label Review when the endpoint handled burst traffic, so the team could improve supervised learning data before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Inference Bias Audit": Inference Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model prediction serving. 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.
“उदाहरण मसौदा: The machine learning team used Inference Bias Audit when the endpoint handled burst traffic, so the team could surface fairness risks before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Dataset Data Split": Dataset Data Split is a ml experimental control that separates examples for training, validation, and testing for labeled and unlabeled data used for learning. 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.
“उदाहरण मसौदा: The machine learning team used Dataset Data Split when the dataset received a new batch, so the team could measure generalization honestly before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”