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기계 지원 번역 초안 (Korean) for "Training Provenance Ledger": Training Provenance Ledger is a ml record that tracks where data came from and how it changed for model learning and optimization workflows. 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.

“예문 초안: The machine learning team used Training Provenance Ledger when the training job restarted, so the team could audit model inputs reliably before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Vector Provenance Ledger": Vector Provenance Ledger is a ml record that tracks where data came from and how it changed for numeric representation and similarity search. 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.

“예문 초안: The machine learning team used Vector Provenance Ledger when the vector store returned close matches, so the team could audit model inputs reliably before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Feature Evaluation Harness": Feature Evaluation Harness is a ml test system that runs repeatable checks against model behavior for input signals used by a machine learning model. 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 Feature Evaluation Harness when a feature distribution shifted, so the team could compare releases with evidence before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Evaluation Safety Filter": Evaluation Safety Filter is a ai policy control that detects content that should be blocked, rewritten, or escalated for AI quality and safety testing. It uses classifiers, rules, and human review queues so teams can keep outputs public-safe while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The AI platform team used Evaluation Safety Filter when a release candidate failed a reasoning scenario, so the team could keep outputs public-safe before the agent workflow reached production.”

기계 지원 번역 초안 (Korean) for "TLS Health Probe": TLS Health Probe is a networking availability check that tests whether a service or path can receive traffic for encrypted transport setup. It uses timed requests, thresholds, and regional checks so teams can send traffic only to healthy targets while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The network engineering team used TLS Health Probe when a certificate neared expiration, so the team could send traffic only to healthy targets before traffic crossed a service boundary.”

기계 지원 번역 초안 (Korean) for "Tag Filter Payload": The Tag Filter Payload is a request or message body that describes how tag filter data moves through PlatPhorm News APIs and feeds. It standardizes requests, responses, article listing metadata, and dictionary payloads for both humans and software agents.

“예문 초안: The developer checked the Tag Filter Payload before sending article or definition data to PlatPhorm.”

기계 지원 번역 초안 (Korean) for "Vector Evaluation Harness": Vector Evaluation Harness is a ml test system that runs repeatable checks against model behavior for numeric representation and similarity search. 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 Vector Evaluation Harness when the vector store returned close matches, so the team could compare releases with evidence before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Alignment Tool Permission": Alignment Tool Permission is a ai access control that decides which tools an AI workflow may call for model behavior shaping and policy fit. It uses operation allowlists, user intent checks, and protected-action gates so teams can block unsafe automation while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The AI platform team used Alignment Tool Permission when the assistant needed a safer answer style, so the team could block unsafe automation before the agent workflow reached production.”

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 "Feature Feature Store": Feature Feature Store is a ml service that serves consistent features to training and inference for input signals used by a machine learning model. 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.

“예문 초안: The machine learning team used Feature Feature Store when a feature distribution shifted, so the team could avoid training-serving skew before the model moved into evaluation.”

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 "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.

“예문 초안: 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.”

기계 지원 번역 초안 (Korean) for "Experiment Calibration Curve": Experiment Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for controlled model comparison. 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 Experiment Calibration Curve when the experiment showed a metric tradeoff, so the team could make confidence scores useful before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Pipeline Drift Monitor": Pipeline Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for automated data and model workflow. 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.

“예문 초안: The machine learning team used Pipeline Drift Monitor when the pipeline missed a validation step, so the team could respond before quality drops before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Routing Response Schema": Routing Response Schema is a ai output contract that requires model output to match a known structure for selection among models, tools, and workflows. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The AI platform team used Routing Response Schema when the router selected a cheaper model, so the team could make responses machine-readable before the agent workflow reached production.”

기계 지원 번역 초안 (Korean) 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.

“예문 초안: 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.”

기계 지원 번역 초안 (Korean) for "Vector Calibration Curve": Vector Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for numeric representation and similarity search. 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 Vector Calibration Curve when the vector store returned close matches, so the team could make confidence scores useful before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Label Evaluation Harness": Label Evaluation Harness is a ml test system that runs repeatable checks against model behavior for ground-truth or weak-supervision annotation. 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 Label Evaluation Harness when the label set had disagreement, so the team could compare releases with evidence before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) 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.

“예문 초안: 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.”

기계 지원 번역 초안 (Korean) for "Model Drift Calibration Curve": Model Drift Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for changes in model performance over time. 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 Model Drift Calibration Curve when the live population changed, so the team could make confidence scores useful before the model moved into evaluation.”