خدمات إعداد وهندسة بيانات الذكاء الاصطناعي

We define the model or evaluation task, intended use, sources, rights, schema, important slices, quality rules, and the person who can accept the result, then collect, transform, split, or label through recorded steps that keep provenance, permissions, guideline versions, and exceptions attached to the work. Coverage, duplicates, leakage, label disagreement, lineage, freshness, and task-specific checks are examined across the slices that actually matter to the use case rather than collapsed into one performance number. The accepted asset is versioned and handed over with its pipeline, controls, known limitations, operating guidance, and ownership, so the next system inherits the decisions instead of re-deriving them.