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 "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.
“उदाहरण मसौदा: 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.”
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 "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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) 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.
“उदाहरण मसौदा: 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.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Training Label Review": Training Label Review is a ml quality workflow that checks annotations for consistency and usefulness for model learning and optimization workflows. 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 Training Label Review when the training job restarted, so the team could improve supervised learning data before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Model Drift Feature Store": Model Drift Feature Store is a ml service that serves consistent features to training and inference for changes in model performance over time. 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 Model Drift Feature Store when the live population changed, so the team could avoid training-serving skew before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Model Drift Bias Audit": Model Drift Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for changes in model performance over time. 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 Model Drift Bias Audit when the live population changed, so the team could surface fairness risks before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Dataset Evaluation Harness": Dataset Evaluation Harness is a ml test system that runs repeatable checks against model behavior for labeled and unlabeled data used for learning. 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 Dataset Evaluation Harness when the dataset received a new batch, so the team could compare releases with evidence before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Embedding Model Card": Embedding Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for vector representation of content or entities. 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 Embedding Model Card when the embedding index changed, so the team could publish model behavior honestly before the model moved into evaluation.”
मशीन-सहायता अनुवाद मसौदा (Hindi) for "Model Drift Embedding Refresh": Model Drift Embedding Refresh is a ml index workflow that updates vector representations after source data changes for changes in model performance over time. 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 Model Drift Embedding Refresh when the live population changed, so the team could keep retrieval results current before the model moved into evaluation.”