Sinopsis
Generative AI rapidly transforms data architectures, reshaping how organizations govern, manage, and analyze data. As organizations face growing demands for real-time insights, scalability, and compliance, traditional data systems struggle to keep pace. Generative AI-powered data architectures offer a shift, enabling intelligent data governance, automated management, and analytics that evolve autonomously. These architectures streamline complex data operations while enhancing decision-making through proactive, AI-driven insights. Further exploration of these infrastructures may reveal more adaptive, explainable, and capable data to support new innovations in enterprise analytics. Generative AI-Powered Data Architectures: From Governance to Autonomous Analytics explores how generative AI can be effectively integrated with modern data architectures to build scalable, secure, and intelligent systems. It addresses the entire data lifecycle from governance and ingestion to transformation, storage, and autonomous analytics empowering organizations to leverage AI-driven processes. This book covers topics such as data analytics, learning systems, and ethics and law, and is a useful resource for engineers, educators, business owners, academicians, researchers, and data scientists.
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Acerca de los autores
Bahaa Eddine Elbaghazaoui is a professor at ENSA Beni Mellal and a PhD holder in Computer Science and Artificial Intelligence. With extensive experience in software engineering, he specializes in full-stack development, microservices, AI integration, and cloud computing. As a project manager and trainer, he has led and contributed to innovative solutions in telecommunications, insurance, and government sectors. His research focuses on data profiling and predictive analytics, with numerous publications in high-impact journals. Passionate about technology and education, he actively mentors, reviews scientific work, and advances cutting-edge software solutions.
Mohamed Amnai is a Full Professor at the Faculty of Sciences, Ibn Tofail University, specializing in Computer Science and Telecommunications. With a Ph.D. in Quality of Service Management and Mobility Control in Ad Hoc Networks, his expertise spans big data, AI-driven decision support systems, machine learning, and network security. Dr. Amnai has contributed extensively to academia as a researcher, supervisor, and journal reviewer, with numerous publications in high-impact journals and conferences. His work focuses on advancing AI applications in data profiling, recommendation systems, and network optimization.
Noreddine Gherabi is an Full Professor of Computer Science at ENSA Khouribga, Sultan Moulay Slimane University. With a Ph.D. in Database Migration and Semantic Web, he specializes in data science, artificial intelligence, semantic technologies, and cybersecurity. He has led significant research on machine learning, recommendation systems, and big data integration, contributing to high-impact publications, books, and conferences. Dr. Gherabi is actively involved in academic leadership, Ph.D. supervision, and scientific committees worldwide.
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Paperback. Condición: new. Paperback. Generative AI rapidly transforms data architectures, reshaping how organizations govern, manage, and analyze data. As organizations face growing demands for real-time insights, scalability, and compliance, traditional data systems struggle to keep pace. Generative AI-powered data architectures offer a shift, enabling intelligent data governance, automated management, and analytics that evolve autonomously. These architectures streamline complex data operations while enhancing decision-making through proactive, AI-driven insights. Further exploration of these infrastructures may reveal more adaptive, explainable, and capable data to support new innovations in enterprise analytics. Generative AI-Powered Data Architectures: From Governance to Autonomous Analytics explores how generative AI can be effectively integrated with modern data architectures to build scalable, secure, and intelligent systems. It addresses the entire data lifecycle from governance and ingestion to transformation, storage, and autonomous analytics empowering organizations to leverage AI-driven processes. This book covers topics such as data analytics, learning systems, and ethics and law, and is a useful resource for engineers, educators, business owners, academicians, researchers, and data scientists. "This book aims to bridge the gap between data architecture and AI advancements by exploring how generative technologies can influence data modeling, orchestration, privacy, and ethics"-- Provided by publisher. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798337356174
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Paperback. Condición: new. Paperback. Generative AI rapidly transforms data architectures, reshaping how organizations govern, manage, and analyze data. As organizations face growing demands for real-time insights, scalability, and compliance, traditional data systems struggle to keep pace. Generative AI-powered data architectures offer a shift, enabling intelligent data governance, automated management, and analytics that evolve autonomously. These architectures streamline complex data operations while enhancing decision-making through proactive, AI-driven insights. Further exploration of these infrastructures may reveal more adaptive, explainable, and capable data to support new innovations in enterprise analytics. Generative AI-Powered Data Architectures: From Governance to Autonomous Analytics explores how generative AI can be effectively integrated with modern data architectures to build scalable, secure, and intelligent systems. It addresses the entire data lifecycle from governance and ingestion to transformation, storage, and autonomous analytics empowering organizations to leverage AI-driven processes. This book covers topics such as data analytics, learning systems, and ethics and law, and is a useful resource for engineers, educators, business owners, academicians, researchers, and data scientists. "This book aims to bridge the gap between data architecture and AI advancements by exploring how generative technologies can influence data modeling, orchestration, privacy, and ethics"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798337356174
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Paperback. Condición: new. Paperback. Generative AI rapidly transforms data architectures, reshaping how organizations govern, manage, and analyze data. As organizations face growing demands for real-time insights, scalability, and compliance, traditional data systems struggle to keep pace. Generative AI-powered data architectures offer a shift, enabling intelligent data governance, automated management, and analytics that evolve autonomously. These architectures streamline complex data operations while enhancing decision-making through proactive, AI-driven insights. Further exploration of these infrastructures may reveal more adaptive, explainable, and capable data to support new innovations in enterprise analytics. Generative AI-Powered Data Architectures: From Governance to Autonomous Analytics explores how generative AI can be effectively integrated with modern data architectures to build scalable, secure, and intelligent systems. It addresses the entire data lifecycle from governance and ingestion to transformation, storage, and autonomous analytics empowering organizations to leverage AI-driven processes. This book covers topics such as data analytics, learning systems, and ethics and law, and is a useful resource for engineers, educators, business owners, academicians, researchers, and data scientists. "This book aims to bridge the gap between data architecture and AI advancements by exploring how generative technologies can influence data modeling, orchestration, privacy, and ethics"-- Provided by publisher. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9798337356174
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