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
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.
“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.”
Rollback Rollback Plan is a devops recovery plan that defines how to return to a known good version for recovery from a bad deployment. It uses version pins, database notes, and operator steps so teams can recover quickly from bad changes while keeping evidence, reliability, and public-safe operational boundaries clear.
“The DevOps team used Rollback Rollback Plan when the error budget started burning, so the team could recover quickly from bad changes before the deployment window opened.”
Artifact Artifact Signature is a devops supply-chain record that proves that an artifact came from an expected build path for build output and package delivery. It uses cryptographic signatures, provenance, and verification so teams can trust deployed packages while keeping evidence, reliability, and public-safe operational boundaries clear.
“The DevOps team used Artifact Artifact Signature when the container image was signed, so the team could trust deployed packages before the deployment window opened.”
CI Approval Step is a devops workflow control that requires review before a sensitive change proceeds for continuous integration workflows. 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.
“The DevOps team used CI Approval Step when a pull request entered the build queue, so the team could keep high-risk automation accountable before the deployment window opened.”
Pipeline Evaluation Harness is a ml test system that runs repeatable checks against model behavior for automated data and model workflow. 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.
“The machine learning team used Pipeline Evaluation Harness when the pipeline missed a validation step, so the team could compare releases with evidence before the model moved into evaluation.”
Environment Runbook Check is a devops operational test that confirms that documented procedures still work for configuration for a runtime stage. It uses dry runs, screenshots, and command validation so teams can keep response playbooks current while keeping evidence, reliability, and public-safe operational boundaries clear.
“The DevOps team used Environment Runbook Check when staging and production drifted, so the team could keep response playbooks current before the deployment window opened.”
Canary Incident Timeline is a devops response record that orders alerts, actions, and decisions during an incident for small-scope production rollout. 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.
“The DevOps team used Canary Incident Timeline when the first traffic slice received the build, so the team could learn from outages without guesswork before the deployment window opened.”
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.
“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.”
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.
“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.”
Fine-Tuning Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for adaptation of a model to a domain. 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.
“The machine learning team used Fine-Tuning Model Card when the fine-tuning run used curated examples, so the team could publish model behavior honestly before the model moved into evaluation.”