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 "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 "Artifact Artifact Signature": Artifact Artifact Signature is a devops supply-chain record that proves that an artifact came from an expected build path for build output and package delivery. It uses cryptographic signatures, provenance, and verification so teams can trust deployed packages while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The DevOps team used Artifact Artifact Signature when the container image was signed, so the team could trust deployed packages 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.”
機械支援の翻訳下書き (Japanese) for "Canary Infra Plan": Canary Infra Plan is a devops change preview that shows expected infrastructure changes before apply for small-scope production rollout. 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 Canary Infra Plan when the first traffic slice received the build, 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 "Rollback Runbook Check": Rollback Runbook Check is a devops operational test that confirms that documented procedures still work for recovery from a bad deployment. 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 Rollback Runbook Check when the error budget started burning, 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 "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 "Rollback Secret Rotation": Rollback Secret Rotation is a devops credential workflow that replaces sensitive keys without service interruption for recovery from a bad deployment. 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 Rollback Secret Rotation when the error budget started burning, so the team could reduce credential exposure before the deployment window opened.”
機械支援の翻訳下書き (Japanese) 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.
“例文の下書き: 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.”
機械支援の翻訳下書き (Japanese) for "Fine-Tuning Feature Store": Fine-Tuning Feature Store is a ml service that serves consistent features to training and inference for adaptation of a model to a domain. 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 Fine-Tuning Feature Store when the fine-tuning run used curated examples, so the team could avoid training-serving skew before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Fine-Tuning Model Card": Fine-Tuning Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for adaptation of a model to a domain. 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.
“例文の下書き: The machine learning team used Fine-Tuning Model Card when the fine-tuning run used curated examples, so the team could publish model behavior honestly before the model moved into evaluation.”