Designing Data Architecture for AI Systems

This comprehensive guide explores the core data architecture challenges behind successful AI initiatives, emphasizing the importance of building a solid data foundation rather than focusing solely on models. It covers essential topics such as the three critical AI pipelines—training, inference, and RAG—data quality, governance, and scalable vector infrastructure, all tailored for enterprise needs and regulated industries. Designed to be vendor-neutral, the book offers reference architectures, migration roadmaps, and practical insights for architects, engineers, and teams aiming to develop resilient, cost-effective AI systems that stand the test of evolving technology and compliance requirements.