Define the new internet.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
2,337 definitions
机器辅助翻译草稿 (Chinese) for "CD Infra Plan": CD Infra Plan is a devops change preview that shows expected infrastructure changes before apply for deployment automation and promotion. 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 CD Infra Plan when the release moved toward production, so the team could review platform changes safely before the deployment window opened.”
机器辅助翻译草稿 (Chinese) for "Environment Rollout Guard": Environment Rollout Guard is a devops release control that limits exposure during gradual deployment for configuration for a runtime stage. 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 Environment Rollout Guard when staging and production drifted, so the team could reduce blast radius before the deployment window opened.”
机器辅助翻译草稿 (Chinese) for "Rollback Incident Timeline": Rollback Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for recovery from a bad deployment. It uses timestamps, owners, and evidence links so teams can learn from outages without guesswork while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The DevOps team used Rollback Incident Timeline when the error budget started burning, so the team could learn from outages without guesswork before the deployment window opened.”
机器辅助翻译草稿 (Chinese) for "Pipeline Feature Store": Pipeline Feature Store is a ml service that serves consistent features to training and inference for automated data and model workflow. 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 Pipeline Feature Store when the pipeline missed a validation step, so the team could avoid training-serving skew before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Pipeline Label Review": Pipeline Label Review is a ml quality workflow that checks annotations for consistency and usefulness for automated data and model workflow. 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 Pipeline Label Review when the pipeline missed a validation step, so the team could improve supervised learning data before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Metric Model Card": Metric Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for measurement of model behavior. 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 Metric Model Card when the metric changed after data cleanup, so the team could publish model behavior honestly 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 "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 "CD Secret Rotation": CD Secret Rotation is a devops credential workflow that replaces sensitive keys without service interruption for deployment automation and promotion. It uses dual credentials, rollout steps, and revocation so teams can reduce credential exposure while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The DevOps team used CD Secret Rotation when the release moved toward production, so the team could reduce credential exposure before the deployment window opened.”
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.”