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.
5,641 definitions
Trace Sticky Note Config Compass is a devops vernacular term for service restart work in a policy-driven service network. It describes a config compass that keeps everyday operations boring in the best possible way, using source labels, trace links, route evidence, and public/protected boundaries that an operator or agent can follow.
Trace Whiteboard Check Runner is a ci/cd vernacular term for workflow run work in a policy-driven service network. It describes a check runner that keeps build, test, and deploy evidence in one explainable path, using source labels, trace links, route evidence, and public/protected boundaries that an operator or agent can follow.
Trace Whiteboard Preview Ticket is a ci/cd vernacular term for preview deploy work in a policy-driven service network. It describes a preview ticket that turns code changes into tested releases without hiding broken steps, using source labels, trace links, route evidence, and public/protected boundaries that an operator or agent can follow.
Trace Whiteboard Route Passport is a policy-driven architecture vernacular term for public DMZ work in a policy-driven service network. It describes a route passport that connects service behavior to written policy instead of vibes, using source labels, trace links, route evidence, and public/protected boundaries that an operator or agent can follow.
Training Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for model learning and optimization workflows. 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.
Training Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for model learning and optimization workflows. 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.
Training Data Split is a ml experimental control that separates examples for training, validation, and testing for model learning and optimization workflows. 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.
Training Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for model learning and optimization workflows. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.
Training Embedding Refresh is a ml index workflow that updates vector representations after source data changes for model learning and optimization workflows. It uses batch jobs, backfills, and index validation so teams can keep retrieval results current while keeping evidence, reliability, and public-safe operational boundaries clear.
Training Evaluation Harness is a ml test system that runs repeatable checks against model behavior for model learning and optimization workflows. 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.