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机器辅助翻译草稿 (Chinese) for "Training Model Card": Training Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for model learning and optimization workflows. 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.

“示例草稿: The machine learning team used Training Model Card when the training job restarted, so the team could publish model behavior honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Pipeline Evaluation Harness": Pipeline Evaluation Harness is a ml test system that runs repeatable checks against model behavior for automated data and model workflow. 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.

“示例草稿: The machine learning team used Pipeline Evaluation Harness when the pipeline missed a validation step, so the team could compare releases with evidence before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Feature Training Checkpoint": Feature Training Checkpoint is a ml recovery artifact that saves model state during learning for input signals used by a machine learning model. 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 Feature Training Checkpoint when a feature distribution shifted, 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 "Experiment Training Checkpoint": Experiment Training Checkpoint is a ml recovery artifact that saves model state during learning for controlled model comparison. 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 Experiment Training Checkpoint when the experiment showed a metric tradeoff, 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 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Runbook Build Gate": Runbook Build Gate is a devops quality gate that blocks promotion when required checks fail for documented operational procedure. 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.

“示例草稿: The DevOps team used Runbook Build Gate when a responder needed the recovery steps, so the team could prevent broken releases before the deployment window opened.”

机器辅助翻译草稿 (Chinese) for "HTTP Failover Policy": HTTP Failover Policy is a networking resilience policy that defines when traffic should move to another path or region for application-layer request routing. 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.

“示例草稿: The network engineering team used HTTP Failover Policy when a client retried a request, so the team could recover from outages predictably before traffic crossed a service boundary.”

机器辅助翻译草稿 (Chinese) for "RAG Context Contract": RAG Context Contract is a ai interface contract that defines what context may be passed into a model call for retrieval-augmented generation pipelines. It uses schemas, redaction rules, source labels, and token budgets so teams can keep model inputs relevant and safe while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The AI platform team used RAG Context Contract when the retriever mixed old and new documents, so the team could keep model inputs relevant and safe before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Pipeline Embedding Refresh": Pipeline Embedding Refresh is a ml index workflow that updates vector representations after source data changes for automated data and model workflow. 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.

“示例草稿: The machine learning team used Pipeline Embedding Refresh when the pipeline missed a validation step, so the team could keep retrieval results current before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Experiment Feature Store": Experiment Feature Store is a ml service that serves consistent features to training and inference for controlled model comparison. 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 Experiment Feature Store when the experiment showed a metric tradeoff, so the team could avoid training-serving skew before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Experiment Drift Monitor": Experiment Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for controlled model comparison. 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 Experiment Drift Monitor when the experiment showed a metric tradeoff, so the team could respond before quality drops before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Model Safety Filter": Model Safety Filter is a ai policy control that detects content that should be blocked, rewritten, or escalated for foundation model behavior and serving. 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.

“示例草稿: The AI platform team used Model Safety Filter when the model produced a low-confidence answer, so the team could keep outputs public-safe before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Tag Search Filter": The Tag Search Filter is a selection constraint for finding tag search information in PlatPhorm News. It improves discovery across article listings, dictionary terms, domains, tags, sources, and AI-readable network metadata.

“示例草稿: The Tag Search Filter surfaced the most relevant article listing from the PlatPhorm feed.”

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 "Vector Bias Audit": Vector Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for numeric representation and similarity search. 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 Vector Bias Audit when the vector store returned close matches, so the team could surface fairness risks before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Scheduler Image Hardening": Scheduler Image Hardening is a compute security practice that reduces risk inside packaged runtime images for placement of work onto resources. It uses minimal bases, patching, and vulnerability checks so teams can ship safer workloads while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The platform engineering team used Scheduler Image Hardening when the cluster needed to place a job, so the team could ship safer workloads before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Experiment Model Card": Experiment Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for controlled model comparison. 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.

“示例草稿: The machine learning team used Experiment Model Card when the experiment showed a metric tradeoff, so the team could publish model behavior honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Experiment Label Review": Experiment Label Review is a ml quality workflow that checks annotations for consistency and usefulness for controlled model comparison. 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 Experiment Label Review when the experiment showed a metric tradeoff, so the team could improve supervised learning data before the model moved into evaluation.”