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
Automatischer Uebersetzungsentwurf (German) for "Artifact Release Manifest": Artifact Release Manifest is a devops delivery record that lists versions, artifacts, routes, and checks for a release for build output and package delivery. It uses commit IDs, checksums, and deployment URLs so teams can make releases auditable while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The DevOps team used Artifact Release Manifest when the container image was signed, so the team could make releases auditable before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Metric Label Review": Metric Label Review is a ml quality workflow that checks annotations for consistency and usefulness for measurement of model behavior. 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.
“Beispielentwurf: The machine learning team used Metric Label Review when the metric changed after data cleanup, so the team could improve supervised learning data before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "CD Infra Plan": CD Infra Plan is a devops change preview that shows expected infrastructure changes before apply for deployment automation and promotion. 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.
“Beispielentwurf: The DevOps team used CD Infra Plan when the release moved toward production, so the team could review platform changes safely before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "CI Trace Link": CI Trace Link is a devops observability link that connects a deployment or workflow to runtime evidence for continuous integration workflows. 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.
“Beispielentwurf: The DevOps team used CI Trace Link when a pull request entered the build queue, so the team could debug production changes faster before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”