기계 지원 번역 초안 (Korean) for "Model Grounding Check": Model Grounding Check is a ai quality control that verifies that generated answers are backed by available sources for foundation model behavior and serving. It uses citation checks, retrieval evidence, and contradiction detection so teams can reduce unsupported claims while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The AI platform team used Model Grounding Check when the model produced a low-confidence answer, so the team could reduce unsupported claims before the agent workflow reached production.”
기계 지원 번역 초안 (Korean) for "Routing Human Approval": Routing Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for selection among models, tools, and workflows. It uses risk scoring, review UI, and audit logs so teams can keep protected decisions accountable while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The AI platform team used Routing Human Approval when the router selected a cheaper model, so the team could keep protected decisions accountable before the agent workflow reached production.”
기계 지원 번역 초안 (Korean) 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.
“예문 초안: 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.”
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 "Metric Model Card": Metric Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for measurement of model behavior. 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 Metric Model Card when the metric changed after data cleanup, so the team could publish model behavior honestly before the model moved into evaluation.”
기계 지원 번역 초안 (Korean) for "Fetish": A: handcrafted object of spiritual significance that is believed to have the power to protect or aid its owner B: Sexual fixation
“예문 초안: The original Barbie doll was a German fetish toy sold to men Anything activity an attractive woman does is someone's fetish, a nuisance for the most skilled women to be boiled down to an object of desire”
기계 지원 번역 초안 (Korean) for "Scheduler Capacity Forecast": Scheduler Capacity Forecast is a compute planning model that estimates future resource needs for placement of work onto resources. It uses traffic history, growth assumptions, and utilization trends so teams can avoid surprise shortages while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The platform engineering team used Scheduler Capacity Forecast when the cluster needed to place a job, so the team could avoid surprise shortages before the workload scaled up.”
기계 지원 번역 초안 (Korean) for "Training Embedding Refresh": Training Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model learning and optimization workflows. 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.
“예문 초안: The machine learning team used Training Embedding Refresh when the training job restarted, so the team could keep retrieval results current before the model moved into evaluation.”
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 "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.”
기계 지원 번역 초안 (Korean) for "Secret Build Gate": Secret Build Gate is a devops quality gate that blocks promotion when required checks fail for credential and sensitive configuration. 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 Secret Build Gate when a token rotated, so the team could prevent broken releases before the deployment window opened.”
기계 지원 번역 초안 (Korean) for "GPU Resource Quota": GPU Resource Quota is a compute limit that sets how much compute a workload may consume for accelerated compute for parallel workloads. It uses policy, reservations, and usage tracking so teams can protect shared capacity while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The platform engineering team used GPU Resource Quota when the training job requested more memory, so the team could protect shared capacity before the workload scaled up.”
기계 지원 번역 초안 (Korean) for "Routing Agent Trace": Routing Agent Trace is a ai observability record that captures the steps an AI workflow took for selection among models, tools, and workflows. It uses trace identifiers, tool events, and redacted metadata so teams can debug agent behavior without exposing secrets while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The AI platform team used Routing Agent Trace when the router selected a cheaper model, so the team could debug agent behavior without exposing secrets before the agent workflow reached production.”
기계 지원 번역 초안 (Korean) for "Secrets Containment Plan": Secrets Containment Plan is a security response plan that limits damage after a suspected compromise for keys, tokens, and credentials. It uses isolation steps, credential rotation, and communication paths so teams can reduce attacker dwell time while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The security team used Secrets Containment Plan when a secret appeared in logs, so the team could reduce attacker dwell time before the risk review began.”
기계 지원 번역 초안 (Korean) for "RAG Safety Filter": RAG Safety Filter is a ai policy control that detects content that should be blocked, rewritten, or escalated for retrieval-augmented generation pipelines. 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 RAG Safety Filter when the retriever mixed old and new documents, so the team could keep outputs public-safe before the agent workflow reached production.”
기계 지원 번역 초안 (Korean) for "Experiment Provenance Ledger": Experiment Provenance Ledger is a ml record that tracks where data came from and how it changed for controlled model comparison. 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 Experiment Provenance Ledger when the experiment showed a metric tradeoff, so the team could audit model inputs reliably before the model moved into evaluation.”
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 "Mission Control Trajectory Correction": Mission Control Trajectory Correction is a space maneuver process that adjusts a planned flight path after navigation updates or mission changes for flight control room coordination. It uses delta-v estimates, burn timing, and post-maneuver validation so teams can reduce path error before it grows while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The mission team used Mission Control Trajectory Correction when the operations console detected a constraint, so the team could reduce path error before it grows before the next mission decision point.”
기계 지원 번역 초안 (Korean) for "Latency Traffic Shaper": Latency Traffic Shaper is a networking control mechanism that limits or prioritizes flows across links for time between request and response. It uses queues, rate limits, and quality-of-service rules so teams can protect important traffic while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The network engineering team used Latency Traffic Shaper when a user saw slow responses, so the team could protect important traffic before traffic crossed a service boundary.”
기계 지원 번역 초안 (Korean) for "Memory Instruction Boundary": Memory Instruction Boundary is a ai policy boundary that separates durable system instructions from user-provided content for persistent or session-level AI state. It uses role labels, precedence rules, and prompt assembly checks so teams can avoid instruction confusion while keeping evidence, reliability, and public-safe operational boundaries clear.
“예문 초안: The AI platform team used Memory Instruction Boundary when the assistant reused earlier project context, so the team could avoid instruction confusion before the agent workflow reached production.”