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机器辅助翻译草稿 (Chinese) for "Model Tool Permission": Model Tool Permission is a ai access control that decides which tools an AI workflow may call for foundation model behavior and serving. 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.

“示例草稿: The AI platform team used Model Tool Permission when the model produced a low-confidence answer, so the team could block unsafe automation before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Storage Isolation Boundary": Storage Isolation Boundary is a compute security boundary that separates workloads so one cannot affect another unexpectedly for persistent data and object access. It uses namespaces, sandboxes, and access controls so teams can reduce cross-workload risk while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The platform engineering team used Storage Isolation Boundary when the workload read a large dataset, so the team could reduce cross-workload risk before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Environment Release Manifest": Environment Release Manifest is a devops delivery record that lists versions, artifacts, routes, and checks for a release for configuration for a runtime stage. It uses commit IDs, checksums, and deployment URLs so teams can make releases auditable while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The DevOps team used Environment Release Manifest when staging and production drifted, so the team could make releases auditable before the deployment window opened.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Environment Rollback Plan": Environment Rollback Plan is a devops recovery plan that defines how to return to a known good version for configuration for a runtime stage. 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.

“示例草稿: The DevOps team used Environment Rollback Plan when staging and production drifted, so the team could recover quickly from bad changes before the deployment window opened.”

机器辅助翻译草稿 (Chinese) for "Feature Bias Audit": Feature Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for input signals used by a machine learning model. 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.

“示例草稿: The machine learning team used Feature Bias Audit when a feature distribution shifted, so the team could surface fairness risks before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Tag Filter Response": The Tag Filter Response is a returned API object 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.

“示例草稿: The developer checked the Tag Filter Response before sending article or definition data to PlatPhorm.”

机器辅助翻译草稿 (Chinese) for "Model Human Approval": Model Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for foundation model behavior and serving. 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.

“示例草稿: The AI platform team used Model Human Approval when the model produced a low-confidence answer, so the team could keep protected decisions accountable before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Embedding Label Review": Embedding Label Review is a ml quality workflow that checks annotations for consistency and usefulness for vector representation of content or entities. 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.

“示例草稿: The machine learning team used Embedding Label Review when the embedding index changed, so the team could improve supervised learning data before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Routing Instruction Boundary": Routing Instruction Boundary is a ai policy boundary that separates durable system instructions from user-provided content for selection among models, tools, and workflows. 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.

“示例草稿: The AI platform team used Routing Instruction Boundary when the router selected a cheaper model, so the team could avoid instruction confusion before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Pipeline Calibration Curve": Pipeline Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for automated data and model workflow. 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.

“示例草稿: The machine learning team used Pipeline Calibration Curve when the pipeline missed a validation step, so the team could make confidence scores useful before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Model Drift Feature Store": Model Drift Feature Store is a ml service that serves consistent features to training and inference for changes in model performance over time. 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.

“示例草稿: The machine learning team used Model Drift Feature Store when the live population changed, so the team could avoid training-serving skew before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Feature Drift Monitor": Feature Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for input signals used by a machine learning model. 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.

“示例草稿: The machine learning team used Feature Drift Monitor when a feature distribution shifted, so the team could respond before quality drops before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Fine-Tuning Drift Monitor": Fine-Tuning Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for adaptation of a model to a domain. 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.

“示例草稿: The machine learning team used Fine-Tuning Drift Monitor when the fine-tuning run used curated examples, so the team could respond before quality drops before the model moved into evaluation.”