Abstract:

The rapid advancement of Artificial Intelligence (AI) technology has introduced novel requirements for data engineering, catalyzing a paradigm shift in data value morphology from data resources to high-quality datasets. This paper systematically analyzes the underlying mechanisms driving the transformation from traditional DataOps to AI-ready data engineering paradigms, and constructs an intelligence-oriented data engineering architecture. The study first delineates the connotation of AI-ready DataOps, conceptualizing it as an engineering methodology that integrates DataOps principles to efficiently, securely, and reliably supply data for AI scenarios. Building upon this foundation, an AI-ready DataOps capability reference framework is proposed, encompassing four critical phases: research and development, delivery, technical operations, and data governance, thereby establishing a clearly defined, step-by-step dataset pipeline. Furthermore, the study elaborates a five-step practical implementation methodology—inventory, construction, processing, governance, and operations—offering actionable guidelines for enterprise-level high-quality dataset construction. This study provides theoretical frameworks and methodological guidance for national high-quality dataset infrastructure development.

Key words: data engineering, DataOps, high-quality dataset, AI, AI-ready DataOps