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
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Artifact Rollout Guard": Artifact Rollout Guard is a devops release control that limits exposure during gradual deployment for build output and package delivery. 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.
“例文の下書き: The DevOps team used Artifact Rollout Guard when the container image was signed, so the team could reduce blast radius before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Rollback Config Drift Check": Rollback Config Drift Check is a devops consistency check that finds differences between intended and live configuration for recovery from a bad deployment. 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.
“例文の下書き: The DevOps team used Rollback Config Drift Check when the error budget started burning, so the team could avoid surprise environment behavior before the deployment window opened.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Pipeline Hyperparameter Sweep": Pipeline Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for automated data and model workflow. It uses bounded search spaces, trial tracking, and early stopping so teams can find better configurations while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The machine learning team used Pipeline Hyperparameter Sweep when the pipeline missed a validation step, so the team could find better configurations before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Environment Rollback Plan": Environment Rollback Plan is a devops recovery plan that defines how to return to a known good version for configuration for a runtime stage. 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.
“例文の下書き: The DevOps team used Environment Rollback Plan when staging and production drifted, so the team could recover quickly from bad changes before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "CI Approval Step": CI Approval Step is a devops workflow control that requires review before a sensitive change proceeds for continuous integration workflows. 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.
“例文の下書き: The DevOps team used CI Approval Step when a pull request entered the build queue, so the team could keep high-risk automation accountable before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Environment Runbook Check": Environment Runbook Check is a devops operational test that confirms that documented procedures still work for configuration for a runtime stage. 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.
“例文の下書き: The DevOps team used Environment Runbook Check when staging and production drifted, so the team could keep response playbooks current before the deployment window opened.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”