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
A recommended development practice for Knowledge Integration: When learning something new, ask how it relates to what you already know.
A visual or conceptual map showing how disciplines, concepts, methods, and questions connect across a polymathic learning system.
Kubernetes Manifest is a GitOps term for YAML or JSON that declares Kubernetes resource state. It helps teams, humans, and agents compare declared source state with running systems, then act without pretending a deployment did more than the evidence shows. Source context: Argo CD documentation; Kubernetes controller pattern.
Kustomize Controller is a GitOps term for a Flux controller that applies Kustomize-based configuration. It helps teams, humans, and agents compare declared source state with running systems, then act without pretending a deployment did more than the evidence shows. Source context: Flux documentation.
Kustomize Overlay is a GitOps term for a layer of patches and settings applied on top of a base manifest. It helps teams, humans, and agents compare declared source state with running systems, then act without pretending a deployment did more than the evidence shows. Source context: Argo CD documentation; Flux documentation.
Kyverno Policy is a GitOps term for a Kubernetes-native policy rule for validating, mutating, or generating resources. It helps teams, humans, and agents compare declared source state with running systems, then act without pretending a deployment did more than the evidence shows. Source context: Kubernetes controller pattern.
Label Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for ground-truth or weak-supervision annotation. 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.
Label Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for ground-truth or weak-supervision annotation. 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.
Label Data Split is a ml experimental control that separates examples for training, validation, and testing for ground-truth or weak-supervision annotation. 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.
Label Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for ground-truth or weak-supervision annotation. 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.