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机器辅助翻译草稿 (Chinese) for "Fine-Tuning Embedding Refresh": Fine-Tuning Embedding Refresh is a ml index workflow that updates vector representations after source data changes for adaptation of a model to a domain. 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 Fine-Tuning Embedding Refresh when the fine-tuning run used curated examples, so the team could keep retrieval results current before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Embedding Bias Audit": Embedding Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for vector representation of content or entities. 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 Embedding Bias Audit when the embedding index changed, so the team could surface fairness risks before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Scheduler Isolation Boundary": Scheduler Isolation Boundary is a compute security boundary that separates workloads so one cannot affect another unexpectedly for placement of work onto resources. 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 Scheduler Isolation Boundary when the cluster needed to place a job, so the team could reduce cross-workload risk before the workload scaled up.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Training Feature Store": Training Feature Store is a ml service that serves consistent features to training and inference for model learning and optimization workflows. 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 Training Feature Store when the training job restarted, 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 "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 "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.

“示例草稿: 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.”

机器辅助翻译草稿 (Chinese) for "Serverless Cold Start Budget": Serverless Cold Start Budget is a compute latency target that limits startup delay for newly scheduled execution for event-driven function execution. It uses prewarming, smaller packages, and runtime tuning so teams can keep first requests responsive while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The platform engineering team used Serverless Cold Start Budget when the function received a traffic burst, so the team could keep first requests responsive before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Environment Infra Plan": Environment Infra Plan is a devops change preview that shows expected infrastructure changes before apply for configuration for a runtime stage. It uses resource graphs, policy checks, and cost notes so teams can review platform changes safely while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The DevOps team used Environment Infra Plan when staging and production drifted, so the team could review platform changes safely before the deployment window opened.”

机器辅助翻译草稿 (Chinese) for "Container Capacity Forecast": Container Capacity Forecast is a compute planning model that estimates future resource needs for packaged application runtime. 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.

“示例草稿: The platform engineering team used Container Capacity Forecast when the image started on a new node, so the team could avoid surprise shortages before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Observability Approval Step": Observability Approval Step is a devops workflow control that requires review before a sensitive change proceeds for logs, metrics, traces, and events. It uses role checks, comments, and audit logs so teams can keep high-risk automation accountable while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The DevOps team used Observability Approval Step when latency increased after deploy, so the team could keep high-risk automation accountable before the deployment window opened.”

机器辅助翻译草稿 (Chinese) for "Label Calibration Curve": Label Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for ground-truth or weak-supervision annotation. 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 Label Calibration Curve when the label set had disagreement, so the team could make confidence scores useful before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "TLS Anycast Endpoint": TLS Anycast Endpoint is a networking routing pattern that advertises one address from multiple locations for encrypted transport setup. It uses regional announcements, health checks, and traffic steering so teams can serve users from nearby healthy sites while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The network engineering team used TLS Anycast Endpoint when a certificate neared expiration, so the team could serve users from nearby healthy sites before traffic crossed a service boundary.”

机器辅助翻译草稿 (Chinese) for "Secret Rollout Guard": Secret Rollout Guard is a devops release control that limits exposure during gradual deployment for credential and sensitive configuration. It uses traffic slices, health checks, and automatic pause rules so teams can reduce blast radius while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The DevOps team used Secret Rollout Guard when a token rotated, so the team could reduce blast radius before the deployment window opened.”

机器辅助翻译草稿 (Chinese) 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.

“示例草稿: 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.”

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 "Environment Approval Step": Environment Approval Step is a devops workflow control that requires review before a sensitive change proceeds for configuration for a runtime stage. It uses role checks, comments, and audit logs so teams can keep high-risk automation accountable while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The DevOps team used Environment Approval Step when staging and production drifted, so the team could keep high-risk automation accountable before the deployment window opened.”

机器辅助翻译草稿 (Chinese) for "Secret Approval Step": Secret Approval Step is a devops workflow control that requires review before a sensitive change proceeds for credential and sensitive configuration. It uses role checks, comments, and audit logs so teams can keep high-risk automation accountable while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The DevOps team used Secret Approval Step when a token rotated, so the team could keep high-risk automation accountable before the deployment window opened.”