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 "Pipeline Model Card": Pipeline Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for automated data and model workflow. 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.
“Beispielentwurf: The machine learning team used Pipeline Model Card when the pipeline missed a validation step, so the team could publish model behavior honestly before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "CD Build Gate": CD Build Gate is a devops quality gate that blocks promotion when required checks fail for deployment automation and promotion. 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.
“Beispielentwurf: The DevOps team used CD Build Gate when the release moved toward production, so the team could prevent broken releases before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Fine-Tuning Provenance Ledger": Fine-Tuning Provenance Ledger is a ml record that tracks where data came from and how it changed for adaptation of a model to a domain. 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 Fine-Tuning Provenance Ledger when the fine-tuning run used curated examples, so the team could audit model inputs reliably before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Artifact Rollout Guard": Artifact Rollout Guard is a devops release control that limits exposure during gradual deployment for build output and package delivery. It uses traffic slices, health checks, and automatic pause rules so teams can reduce blast radius while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The DevOps team used Artifact Rollout Guard when the container image was signed, so the team could reduce blast radius before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "Environment Release Manifest": Environment Release Manifest is a devops delivery record that lists versions, artifacts, routes, and checks for a release for configuration for a runtime stage. 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 Environment Release Manifest when staging and production drifted, so the team could make releases auditable before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Canary Config Drift Check": Canary Config Drift Check is a devops consistency check that finds differences between intended and live configuration for small-scope production rollout. 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.
“Beispielentwurf: The DevOps team used Canary Config Drift Check when the first traffic slice received the build, so the team could avoid surprise environment behavior before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”