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
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) for "Environment Incident Timeline": Environment Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for configuration for a runtime stage. 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.
“Exemplo em rascunho: The DevOps team used Environment Incident Timeline when staging and production drifted, so the team could learn from outages without guesswork before the deployment window opened.”
Rascunho de traducao automatica (Portuguese) for "Canary Build Gate": Canary Build Gate is a devops quality gate that blocks promotion when required checks fail for small-scope production rollout. 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.
“Exemplo em rascunho: The DevOps team used Canary Build Gate when the first traffic slice received the build, so the team could prevent broken releases before the deployment window opened.”
Rascunho de traducao automatica (Portuguese) for "CI Build Gate": CI Build Gate is a devops quality gate that blocks promotion when required checks fail for continuous integration workflows. 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.
“Exemplo em rascunho: The DevOps team used CI Build Gate when a pull request entered the build queue, so the team could prevent broken releases before the deployment window opened.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: The DevOps team used Environment Release Manifest when staging and production drifted, so the team could make releases auditable before the deployment window opened.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) 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.
“Exemplo em rascunho: 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.”
Rascunho de traducao automatica (Portuguese) for "CD Config Drift Check": CD Config Drift Check is a devops consistency check that finds differences between intended and live configuration for deployment automation and promotion. 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.
“Exemplo em rascunho: The DevOps team used CD Config Drift Check when the release moved toward production, so the team could avoid surprise environment behavior before the deployment window opened.”
Rascunho de traducao automatica (Portuguese) for "Fine-Tuning Bias Audit": Fine-Tuning Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for adaptation of a model to a domain. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.
“Exemplo em rascunho: The machine learning team used Fine-Tuning Bias Audit when the fine-tuning run used curated examples, so the team could surface fairness risks before the model moved into evaluation.”