{"product_id":"data-engineering-for-multimodal-ai-architecting-scalable-systems-for-next-generation-ai-applications-paperback","title":"Data Engineering for Multimodal AI: Architecting Scalable Systems for Next-Generation AI Applications - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eVasundra Srinivasan\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAI 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.\u003c\/p\u003e \u003cp\u003e\u003cem\u003eData Engineering for Multimodal AI\u003c\/em\u003e 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.\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eDesign and implement cloud-native data architectures optimized for multimodal AI workloads\u003c\/li\u003e \u003cli\u003eBuild efficient and scalable ETL processes for preparing diverse AI training data\u003c\/li\u003e \u003cli\u003eImplement real-time data processing pipelines for multimodal AI inference\u003c\/li\u003e \u003cli\u003eDevelop and manage feature stores that support multiple data modalities\u003c\/li\u003e \u003cli\u003eApply data governance and security practices specific to multimodal AI projects\u003c\/li\u003e \u003cli\u003eOptimize data storage and retrieval for various types of multimodal ML models\u003c\/li\u003e \u003cli\u003eIntegrate data versioning and lineage tracking in multimodal AI workflows\u003c\/li\u003e \u003cli\u003eImplement data-quality frameworks to ensure reliable outcomes across data types\u003c\/li\u003e \u003cli\u003eDesign data pipelines that support responsible AI practices in a multimodal context\u003c\/li\u003e\n\u003c\/ul\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 546\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.11 x 9.19 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 08, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":50530031108344,"sku":"9781098190781","price":86.47,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0698\/5629\/7208\/files\/DmTM48mchv9781098190781.webp?v=1789638635","url":"https:\/\/barneysbooksellers.com\/products\/data-engineering-for-multimodal-ai-architecting-scalable-systems-for-next-generation-ai-applications-paperback","provider":"Barney's Book Sellers","version":"1.0","type":"link"}