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 Hyperparameter Sweep": Pipeline Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for automated data and model workflow. It uses bounded search spaces, trial tracking, and early stopping so teams can find better configurations while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The machine learning team used Pipeline Hyperparameter Sweep when the pipeline missed a validation step, so the team could find better configurations before the model moved into evaluation.”
Automatischer Uebersetzungsentwurf (German) for "Rollback Build Gate": Rollback Build Gate is a devops quality gate that blocks promotion when required checks fail for recovery from a bad deployment. 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 Rollback Build Gate when the error budget started burning, so the team could prevent broken releases before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "Canary Approval Step": Canary Approval Step is a devops workflow control that requires review before a sensitive change proceeds for small-scope production rollout. It uses role checks, comments, and audit logs so teams can keep high-risk automation accountable while keeping evidence, reliability, and public-safe operational boundaries clear.
“Beispielentwurf: The DevOps team used Canary Approval Step when the first traffic slice received the build, so the team could keep high-risk automation accountable before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”
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 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 "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.
“Beispielentwurf: 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.”
Automatischer Uebersetzungsentwurf (German) for "CI Infra Plan": CI Infra Plan is a devops change preview that shows expected infrastructure changes before apply for continuous integration workflows. 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 CI Infra Plan when a pull request entered the build queue, so the team could review platform changes safely before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) for "CD Incident Timeline": CD Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for deployment automation and promotion. 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.
“Beispielentwurf: The DevOps team used CD Incident Timeline when the release moved toward production, so the team could learn from outages without guesswork before the deployment window opened.”
Automatischer Uebersetzungsentwurf (German) 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.
“Beispielentwurf: 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.”