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 "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 "Artifact Approval Step": Artifact Approval Step is a devops workflow control that requires review before a sensitive change proceeds for build output and package delivery. 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 Artifact Approval Step when the container image was signed, so the team could keep high-risk automation accountable before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Rollback Infra Plan": Rollback Infra Plan is a devops change preview that shows expected infrastructure changes before apply for recovery from a bad deployment. 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.
“Beispielentwurf: The DevOps team used Rollback Infra Plan when the error budget started burning, so the team could review platform changes safely before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
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 "CI Rollout Guard": CI Rollout Guard is a devops release control that limits exposure during gradual deployment for continuous integration workflows. 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.
“Beispielentwurf: The DevOps team used CI Rollout Guard when a pull request entered the build queue, so the team could reduce blast radius before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Environment Artifact Signature": Environment Artifact Signature is a devops supply-chain record that proves that an artifact came from an expected build path for configuration for a runtime stage. It uses cryptographic signatures, provenance, and verification so teams can trust deployed packages while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The DevOps team used Environment Artifact Signature when staging and production drifted, so the team could trust deployed packages before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
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 "Pipeline Bias Audit": Pipeline Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for automated data and model workflow. 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.
“Beispielentwurf: The machine learning team used Pipeline Bias Audit when the pipeline missed a validation step, so the team could surface fairness risks before the model moved into evaluation.”