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
Automatischer Uebersetzungsentwurf (German) for "CD Trace Link": CD Trace Link is a devops observability link that connects a deployment or workflow to runtime evidence for deployment automation and promotion. It uses trace IDs, span metadata, and release identifiers so teams can debug production changes faster while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The DevOps team used CD Trace Link when the release moved toward production, so the team could debug production changes faster before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Fine-Tuning Calibration Curve": Fine-Tuning Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for adaptation of a model to a domain. 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.
“Beispielentwurf: The machine learning team used Fine-Tuning Calibration Curve when the fine-tuning run used curated examples, so the team could make confidence scores useful before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Rollback Approval Step": Rollback Approval Step is a devops workflow control that requires review before a sensitive change proceeds for recovery from a bad deployment. 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.
“Beispielentwurf: The DevOps team used Rollback Approval Step when the error budget started burning, so the team could keep high-risk automation accountable before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Environment Build Gate": Environment Build Gate is a devops quality gate that blocks promotion when required checks fail for configuration for a runtime stage. 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.
“Beispielentwurf: The DevOps team used Environment Build Gate when staging and production drifted, so the team could prevent broken releases before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Pipeline Data Split": Pipeline Data Split is a ml experimental control that separates examples for training, validation, and testing for automated data and model workflow. It uses randomization rules, leakage checks, and seed tracking so teams can measure generalization honestly while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Pipeline Data Split when the pipeline missed a validation step, so the team could measure generalization honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Metric Calibration Curve": Metric Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for measurement of model behavior. 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.
“Beispielentwurf: The machine learning team used Metric Calibration Curve when the metric changed after data cleanup, so the team could make confidence scores useful before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Metric Data Split": Metric Data Split is a ml experimental control that separates examples for training, validation, and testing for measurement of model behavior. It uses randomization rules, leakage checks, and seed tracking so teams can measure generalization honestly while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Metric Data Split when the metric changed after data cleanup, so the team could measure generalization honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Artifact Trace Link": Artifact Trace Link is a devops observability link that connects a deployment or workflow to runtime evidence for build output and package delivery. It uses trace IDs, span metadata, and release identifiers so teams can debug production changes faster while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The DevOps team used Artifact Trace Link when the container image was signed, so the team could debug production changes faster before the deployment window opened.”