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AI is only as capable as the context it’s given, and context is a data engineering problem before it’s a model problem. As multimodal AI systems and applications become increasingly sophisticated and data-hungry, the infrastructure that produces and governs that context has to evolve to keep pace.
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 that multimodal AI systems and applications depend on for effective context engineering. 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
AI is only as capable as the context it’s given, and context is a data engineering problem before it’s a model problem. As multimodal AI systems and applications become increasingly sophisticated and data-hungry, the infrastructure that produces and governs that context has to evolve to keep pace.
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 that multimodal AI systems and applications depend on for effective context engineering. 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
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