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
기계 지원 번역 초안 (Korean) for "Fine-Tuning Model Card": Fine-Tuning Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for adaptation of a model to a domain. 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.
“예문 초안: The machine learning team used Fine-Tuning Model Card when the fine-tuning run used curated examples, so the team could publish model behavior honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Metric Calibration Curve": Metric Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for measurement of model behavior. 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.
“예문 초안: The machine learning team used Metric Calibration Curve when the metric changed after data cleanup, so the team could make confidence scores useful before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Pipeline Model Card": Pipeline Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for automated data and model workflow. 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.
“예문 초안: The machine learning team used Pipeline Model Card when the pipeline missed a validation step, so the team could publish model behavior honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Fine-Tuning Label Review": Fine-Tuning Label Review is a ml quality workflow that checks annotations for consistency and usefulness for adaptation of a model to a domain. 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.
“예문 초안: The machine learning team used Fine-Tuning Label Review when the fine-tuning run used curated examples, so the team could improve supervised learning data before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Environment Rollout Guard": Environment Rollout Guard is a devops release control that limits exposure during gradual deployment for configuration for a runtime stage. It uses traffic slices, health checks, and automatic pause rules so teams can reduce blast radius while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The DevOps team used Environment Rollout Guard when staging and production drifted, so the team could reduce blast radius before the deployment window opened.”
기계 지원 번역 초안 (Korean) for "CI Build Gate": CI Build Gate is a devops quality gate that blocks promotion when required checks fail for continuous integration workflows. It uses tests, lint, security scans, and policy rules so teams can prevent broken releases while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The DevOps team used CI Build Gate when a pull request entered the build queue, so the team could prevent broken releases before the deployment window opened.”
기계 지원 번역 초안 (Korean) for "Fine-Tuning Evaluation Harness": Fine-Tuning Evaluation Harness is a ml test system that runs repeatable checks against model behavior for adaptation of a model to a domain. 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.
“예문 초안: The machine learning team used Fine-Tuning Evaluation Harness when the fine-tuning run used curated examples, so the team could compare releases with evidence before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Pipeline Hyperparameter Sweep": Pipeline Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for automated data and model workflow. 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.
“예문 초안: The machine learning team used Pipeline Hyperparameter Sweep when the pipeline missed a validation step, so the team could find better configurations before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Environment Incident Timeline": Environment Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for configuration for a runtime stage. It uses timestamps, owners, and evidence links so teams can learn from outages without guesswork while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The DevOps team used Environment Incident Timeline when staging and production drifted, so the team could learn from outages without guesswork before the deployment window opened.”
기계 지원 번역 초안 (Korean) for "Pipeline Label Review": Pipeline Label Review is a ml quality workflow that checks annotations for consistency and usefulness for automated data and model workflow. 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.
“예문 초안: The machine learning team used Pipeline Label Review when the pipeline missed a validation step, so the team could improve supervised learning data before the model moved into evaluation.”