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 "CD Trace Link": CD Trace Link is a devops observability link that connects a deployment or workflow to runtime evidence for deployment automation and promotion. It uses trace IDs, span metadata, and release identifiers so teams can debug production changes faster while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The DevOps team used CD Trace Link when the release moved toward production, so the team could debug production changes faster before the deployment window opened.”
機械支援の翻訳下書き (Japanese) for "Pipeline Provenance Ledger": Pipeline Provenance Ledger is a ml record that tracks where data came from and how it changed for automated data and model workflow. It uses hashes, source labels, and transformation history so teams can audit model inputs reliably while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The machine learning team used Pipeline Provenance Ledger when the pipeline missed a validation step, so the team could audit model inputs reliably before the model moved into evaluation.”
機械支援の翻訳下書き (Japanese) for "Canary Release Manifest": Canary Release Manifest is a devops delivery record that lists versions, artifacts, routes, and checks for a release for small-scope production rollout. 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 Canary Release Manifest when the first traffic slice received the build, 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 Training Checkpoint": Metric Training Checkpoint is a ml recovery artifact that saves model state during learning for measurement of model behavior. 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 Metric Training Checkpoint when the metric changed after data cleanup, 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 Config Drift Check": Environment Config Drift Check is a devops consistency check that finds differences between intended and live configuration for configuration for a runtime stage. 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 Environment Config Drift Check when staging and production drifted, 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 "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 "CI Incident Timeline": CI Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for continuous integration workflows. 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 CI Incident Timeline when a pull request entered the build queue, 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 "Fine-Tuning Evaluation Harness": Fine-Tuning Evaluation Harness is a ml test system that runs repeatable checks against model behavior for adaptation of a model to a domain. 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 Fine-Tuning Evaluation Harness when the fine-tuning run used curated examples, 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 "CI Config Drift Check": CI Config Drift Check is a devops consistency check that finds differences between intended and live configuration for continuous integration workflows. 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 CI Config Drift Check when a pull request entered the build queue, 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 Trace Link": Artifact Trace Link is a devops observability link that connects a deployment or workflow to runtime evidence for build output and package delivery. It uses trace IDs, span metadata, and release identifiers so teams can debug production changes faster while keeping evidence, reliability, and public-safe operational boundaries clear.
“例文の下書き: The DevOps team used Artifact Trace Link when the container image was signed, so the team could debug production changes faster before the deployment window opened.”