Data Engineering for Multimodal AI: Architecting Scalable Systems for Next-Generation AI Applications

A shift is underway in how organizations approach data infrastructure for AI-driven transformation. As multimodal AI systems and applications become increasingly sophisticated and data hungry, data systems must evolve to meet these complex demands.

Data Engineering for Multimodal AI is one of the first practical guides for data engineers, machine learning engineers, and MLOps specialists looking to rapidly master the skills needed to build robust, scalable data infrastructures for multimodal AI systems and applications. You'll follow the entire lifecycle of AI-driven data engineering, from conceptualizing data architectures to implementing data pipelines optimized for multimodal learning in both cloud native and on-premises environments. And each chapter includes step-by-step guides and best practices for implementing key concepts.

• Design and implement cloud native data architectures optimized for multimodal AI workloads
• Build efficient and scalable ETL processes for preparing diverse AI training data
• Implement real-time data processing pipelines for multimodal AI inference
• Develop and manage feature stores that support multiple data modalities
• Apply data governance and security practices specific to multimodal AI projects
• Optimize data storage and retrieval for various types of multimodal ML models
• Integrate data versioning and lineage tracking in multimodal AI workflows
• Implement data-quality frameworks to ensure reliable outcomes across data types
• Design data pipelines that support responsible AI practices in a multimodal context