YAML - Page 5

YAML is a human-readable data-serialization format commonly used for configuration files, machine learning pipelines, deployment manifests, and automation workflows. It represents mappings, lists, and scalar values through indentation and concise syntax. AI projects may use YAML to record model settings, dataset paths, experiment parameters, service permissions, or orchestration steps so configurations can be reviewed and versioned separately from code. Indentation mistakes, implicit data types, duplicate keys, and unsafe parsing can cause subtle failures. YAML itself is not an AI technology, but reliable AI operations depend on treating configuration as controlled software: validate schemas, restrict secrets, review changes, and use safe parsers rather than executing untrusted tags or content.