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机器辅助翻译草稿 (Chinese) for "Training Evaluation Harness": Training Evaluation Harness is a ml test system that runs repeatable checks against model behavior for model learning and optimization workflows. 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 Training Evaluation Harness when the training job restarted, 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 Model Card": Pipeline Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for automated data and model workflow. 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 Pipeline Model Card when the pipeline missed a validation step, so the team could publish model behavior honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Rollback Rollback Plan": Rollback Rollback Plan is a devops recovery plan that defines how to return to a known good version for recovery from a bad deployment. 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 Rollback Rollback Plan when the error budget started burning, so the team could recover quickly from bad changes before the deployment window opened.”

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 "Evaluation Human Approval": Evaluation Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for AI quality and safety testing. 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 Evaluation Human Approval when a release candidate failed a reasoning scenario, so the team could keep protected decisions accountable 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 "Routing Response Schema": Routing Response Schema is a ai output contract that requires model output to match a known structure for selection among models, tools, and workflows. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The AI platform team used Routing Response Schema when the router selected a cheaper model, so the team could make responses machine-readable before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Storage Checkpoint Restore": Storage Checkpoint Restore is a compute recovery workflow that resumes work from a saved state for persistent data and object access. It uses snapshots, state files, and integrity checks so teams can recover long-running work while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The platform engineering team used Storage Checkpoint Restore when the workload read a large dataset, so the team could recover long-running work before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Dataset Bias Audit": Dataset Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for labeled and unlabeled data used for learning. 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 Dataset Bias Audit when the dataset received a new batch, so the team could surface fairness risks before the model moved into evaluation.”

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 "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 "Vector Calibration Curve": Vector Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for numeric representation and similarity search. 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 Vector Calibration Curve when the vector store returned close matches, so the team could make confidence scores useful before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Label Drift Monitor": Label Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for ground-truth or weak-supervision annotation. 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 Label Drift Monitor when the label set had disagreement, so the team could respond before quality drops before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Rollback Build Gate": Rollback Build Gate is a devops quality gate that blocks promotion when required checks fail for recovery from a bad deployment. 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 Rollback Build Gate when the error budget started burning, so the team could prevent broken releases before the deployment window opened.”

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 "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 "Training Training Checkpoint": Training Training Checkpoint is a ml recovery artifact that saves model state during learning for model learning and optimization workflows. 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 Training Training Checkpoint when the training job restarted, 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 "Fine-Tuning Label Review": Fine-Tuning Label Review is a ml quality workflow that checks annotations for consistency and usefulness for adaptation of a model to a domain. 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 Fine-Tuning Label Review when the fine-tuning run used curated examples, so the team could improve supervised learning data before the model moved into evaluation.”

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 "Fine-Tuning Evaluation Harness": Fine-Tuning Evaluation Harness is a ml test system that runs repeatable checks against model behavior for adaptation of a model to a domain. 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 Fine-Tuning Evaluation Harness when the fine-tuning run used curated examples, so the team could compare releases with evidence before the model moved into evaluation.”