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 "Label Embedding Refresh": Label Embedding Refresh is a ml index workflow that updates vector representations after source data changes for ground-truth or weak-supervision annotation. 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.
“उदाहरण मसौदा: The machine learning team used Label Embedding Refresh when the label set had disagreement, so the team could keep retrieval results current before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Label Hyperparameter Sweep": Label Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for ground-truth or weak-supervision annotation. 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.
“उदाहरण मसौदा: The machine learning team used Label Hyperparameter Sweep when the label set had disagreement, so the team could find better configurations before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
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 "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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Experiment Label Review": Experiment Label Review is a ml quality workflow that checks annotations for consistency and usefulness for controlled model comparison. 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 Experiment Label Review when the experiment showed a metric tradeoff, so the team could improve supervised learning data before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Metric Feature Store": Metric Feature Store is a ml service that serves consistent features to training and inference for measurement of model behavior. 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.
“उदाहरण मसौदा: The machine learning team used Metric Feature Store when the metric changed after data cleanup, so the team could avoid training-serving skew before the model moved into evaluation.”