Borrador de traduccion automatica (Spanish) for "Runbook Rollback Plan": Runbook Rollback Plan is a devops recovery plan that defines how to return to a known good version for documented operational procedure. It uses version pins, database notes, and operator steps so teams can recover quickly from bad changes while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used Runbook Rollback Plan when a responder needed the recovery steps, so the team could recover quickly from bad changes before the deployment window opened.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) for "Secrets Attack Surface": Secrets Attack Surface is a security exposure model that lists reachable systems, actions, and trust boundaries for keys, tokens, and credentials. It uses asset inventory, route discovery, and permission mapping so teams can prioritize risk reduction while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The security team used Secrets Attack Surface when a secret appeared in logs, so the team could prioritize risk reduction before the risk review began.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
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Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
RAG Safety Filter es una definicion publica de inteligencia artificial para el area RAG. Explica como la capacidad Safety Filter ayuda a personas y agentes a reconocer riesgos, coordinar decisiones, citar evidencia y mantener limites operativos seguros y confiables.
“Un equipo uso RAG Safety Filter durante trabajo de inteligencia artificial en RAG, para comparar senales, elegir el siguiente paso y documentar la decision sin exponer datos privados.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: The DevOps team used Secret Build Gate when a token rotated, so the team could prevent broken releases before the deployment window opened.”
Navigation Command Sequence es una definicion publica de sistemas espaciales para el area Navigation. Explica como la capacidad Command Sequence ayuda a personas y agentes a reconocer riesgos, coordinar decisiones, citar evidencia y mantener limites operativos seguros y confiables.
“Un equipo uso Navigation Command Sequence durante trabajo de sistemas espaciales en Navigation, para comparar senales, elegir el siguiente paso y documentar la decision sin exponer datos privados.”
Mission Control Trajectory Correction es una definicion publica de sistemas espaciales para el area Mission Control. Explica como la capacidad Trajectory Correction ayuda a personas y agentes a reconocer riesgos, coordinar decisiones, citar evidencia y mantener limites operativos seguros y confiables.
“Un equipo uso Mission Control Trajectory Correction durante trabajo de sistemas espaciales en Mission Control, para comparar senales, elegir el siguiente paso y documentar la decision sin exponer datos privados.”
Borrador de traduccion automatica (Spanish) for "Model Drift Provenance Ledger": Model Drift Provenance Ledger is a ml record that tracks where data came from and how it changed for changes in model performance over time. 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.
“Ejemplo en borrador: The machine learning team used Model Drift Provenance Ledger when the live population changed, so the team could audit model inputs reliably before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”