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 Secret Rotation": Artifact Secret Rotation is a devops credential workflow that replaces sensitive keys without service interruption for build output and package delivery. 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 Artifact Secret Rotation when the container image was signed, so the team could reduce credential exposure before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Pipeline Evaluation Harness": Pipeline Evaluation Harness is a ml test system that runs repeatable checks against model behavior for automated data and model workflow. It uses fixtures, metrics, thresholds, and regression reports so teams can compare releases with evidence while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The machine learning team used Pipeline Evaluation Harness when the pipeline missed a validation step, so the team could compare releases with evidence before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Artifact Build Gate": Artifact Build Gate is a devops quality gate that blocks promotion when required checks fail for build output and package delivery. 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 Artifact Build Gate when the container image was signed, 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 Training Checkpoint": Fine-Tuning Training Checkpoint is a ml recovery artifact that saves model state during learning for adaptation of a model to a domain. 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.
“例文の下書き: The machine learning team used Fine-Tuning Training Checkpoint when the fine-tuning run used curated examples, so the team could resume or inspect training safely before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "CD Rollback Plan": CD Rollback Plan is a devops recovery plan that defines how to return to a known good version for deployment automation and promotion. 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 CD Rollback Plan when the release moved toward production, 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 "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 "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.
“例文の下書き: The DevOps team used Environment Build Gate when staging and production drifted, so the team could prevent broken releases before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "CI Release Manifest": CI Release Manifest is a devops delivery record that lists versions, artifacts, routes, and checks for a release for continuous integration workflows. It uses commit IDs, checksums, and deployment URLs so teams can make releases auditable while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The DevOps team used CI Release Manifest when a pull request entered the build queue, so the team could make releases auditable before the deployment window opened.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”