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 "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 "Rollback Runbook Check": Rollback Runbook Check is a devops operational test that confirms that documented procedures still work for recovery from a bad deployment. It uses dry runs, screenshots, and command validation so teams can keep response playbooks current while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The DevOps team used Rollback Runbook Check when the error budget started burning, so the team could keep response playbooks current 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 "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 "Pipeline Provenance Ledger": Pipeline Provenance Ledger is a ml record that tracks where data came from and how it changed for automated data and model workflow. 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 Pipeline Provenance Ledger when the pipeline missed a validation step, so the team could audit model inputs reliably before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Metric Label Review": Metric Label Review is a ml quality workflow that checks annotations for consistency and usefulness for measurement of model behavior. 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 Metric Label Review when the metric changed after data cleanup, so the team could improve supervised learning data before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Artifact Config Drift Check": Artifact Config Drift Check is a devops consistency check that finds differences between intended and live configuration for build output and package delivery. It uses desired state, live state, and diff reports so teams can avoid surprise environment behavior while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The DevOps team used Artifact Config Drift Check when the container image was signed, so the team could avoid surprise environment behavior 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 "Metric Training Checkpoint": Metric Training Checkpoint is a ml recovery artifact that saves model state during learning for measurement of model behavior. 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 Metric Training Checkpoint when the metric changed after data cleanup, 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 "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.”