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 "Artifact Incident Timeline": Artifact Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for build output and package delivery. 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.
“例文の下書き: The DevOps team used Artifact Incident Timeline when the container image was signed, so the team could learn from outages without guesswork before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Artifact Infra Plan": Artifact Infra Plan is a devops change preview that shows expected infrastructure changes before apply for build output and package delivery. 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.
“例文の下書き: The DevOps team used Artifact Infra Plan when the container image was signed, so the team could review platform changes safely before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Environment Secret Rotation": Environment Secret Rotation is a devops credential workflow that replaces sensitive keys without service interruption for configuration for a runtime stage. It uses dual credentials, rollout steps, and revocation so teams can reduce credential exposure while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The DevOps team used Environment Secret Rotation when staging and production drifted, so the team could reduce credential exposure before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Canary Build Gate": Canary Build Gate is a devops quality gate that blocks promotion when required checks fail for small-scope production rollout. 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.
“例文の下書き: The DevOps team used Canary Build Gate when the first traffic slice received the build, so the team could prevent broken releases before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Fine-Tuning Bias Audit": Fine-Tuning Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for adaptation of a model to a domain. 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.
“例文の下書き: The machine learning team used Fine-Tuning Bias Audit when the fine-tuning run used curated examples, so the team could surface fairness risks before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Fine-Tuning Embedding Refresh": Fine-Tuning Embedding Refresh is a ml index workflow that updates vector representations after source data changes for adaptation of a model to a domain. 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.
“例文の下書き: The machine learning team used Fine-Tuning Embedding Refresh when the fine-tuning run used curated examples, so the team could keep retrieval results current before the model moved into evaluation.”