SKU/Artículo: AMZ-1098190785
Data Engineering for Multimodal AI: Architecting Scalable Systems for Next-Generation AI Applications
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En stock
En stock
Peso con empaque:
1.54 kg
1.54 kg
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No
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- 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 workloadsBuild efficient and scalable ETL processes for preparing diverse AI training dataImplement real-time data processing pipelines for multimodal AI inferenceDevelop and manage feature stores that support multiple data modalitiesApply data governance and security practices specific to multimodal AI projectsOptimize data storage and retrieval for various types of multimodal ML modelsIntegrate data versioning and lineage tracking in multimodal AI workflowsImplement data-quality frameworks to ensure reliable outcomes across data typesDesign data pipelines that support responsible AI practices in a multimodal context
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AR$169.713
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AR$330.933
49% OFF
AR$169.713
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