Borrador de traduccion automatica (Spanish) for "Fine-Tuning Feature Store": Fine-Tuning Feature Store is a ml service that serves consistent features to training and inference for adaptation of a model to a domain. 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.
“Ejemplo en borrador: The machine learning team used Fine-Tuning Feature Store when the fine-tuning run used curated examples, so the team could avoid training-serving skew before the model moved into evaluation.”
Model Safety Filter es una definicion publica de inteligencia artificial para el area Model. 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 Model Safety Filter durante trabajo de inteligencia artificial en Model, para comparar senales, elegir el siguiente paso y documentar la decision sin exponer datos privados.”
Borrador de traduccion automatica (Spanish) 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.
“Ejemplo en borrador: 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.”
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 "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.”
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 "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 Resource Quota": Scheduler Resource Quota is a compute limit that sets how much compute a workload may consume for placement of work onto resources. 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 Scheduler Resource Quota when the cluster needed to place a job, so the team could protect shared capacity before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Secrets Evidence Chain": Secrets Evidence Chain is a security audit record that preserves how security evidence was collected and handled for keys, tokens, and credentials. It uses timestamps, hashes, owners, and storage controls so teams can support trustworthy investigation while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The security team used Secrets Evidence Chain when a secret appeared in logs, so the team could support trustworthy investigation before the risk review began.”
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 "Metric Provenance Ledger": Metric Provenance Ledger is a ml record that tracks where data came from and how it changed for measurement of model behavior. 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 Metric Provenance Ledger when the metric changed after data cleanup, so the team could audit model inputs reliably before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Edge Isolation Boundary": Edge Isolation Boundary is a compute security boundary that separates workloads so one cannot affect another unexpectedly for globally distributed runtime. It uses namespaces, sandboxes, and access controls so teams can reduce cross-workload risk while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Edge Isolation Boundary when the request arrived near a user, so the team could reduce cross-workload risk before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Label Provenance Ledger": Label Provenance Ledger is a ml record that tracks where data came from and how it changed for ground-truth or weak-supervision annotation. 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 Label Provenance Ledger when the label set had disagreement, so the team could audit model inputs reliably before the model moved into evaluation.”
Borrador de traduccion automatica (Spanish) for "Fine-Tuning Bias Audit": Fine-Tuning Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for adaptation of a model to a domain. 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.
“Ejemplo en borrador: The machine learning team used Fine-Tuning Bias Audit when the fine-tuning run used curated examples, so the team could surface fairness risks 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 "Edge Backpressure Control": Edge Backpressure Control is a compute stability pattern that slows incoming work when downstream capacity is limited for globally distributed runtime. It uses queues, retry budgets, and admission control so teams can avoid overload cascades while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Edge Backpressure Control when the request arrived near a user, so the team could avoid overload cascades before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "DNS Failover Policy": DNS Failover Policy is a networking resilience policy that defines when traffic should move to another path or region for name resolution and delegation. It uses health signals, priorities, and cooldown windows so teams can recover from outages predictably while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The network engineering team used DNS Failover Policy when a resolver returned stale data, so the team could recover from outages predictably before traffic crossed a service boundary.”
Borrador de traduccion automatica (Spanish) for "Guardrail Agent Trace": Guardrail Agent Trace is a ai observability record that captures the steps an AI workflow took for policy controls around model input and output. 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 Guardrail Agent Trace when the model tried to include private context, so the team could debug agent behavior without exposing secrets before the agent workflow reached production.”
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.”
Borrador de traduccion automatica (Spanish) for "Identity Evidence Chain": Identity Evidence Chain is a security audit record that preserves how security evidence was collected and handled for user and workload identity. It uses timestamps, hashes, owners, and storage controls so teams can support trustworthy investigation while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The security team used Identity Evidence Chain when a service account requested access, so the team could support trustworthy investigation before the risk review began.”