Articles liés à Vector Databases for Generative AI Applications
Synopsis
"Vector Databases for Generative AI Applications" explores the intersection of two cutting-edge fields: vector databases and generative artificial intelligence (AI). The book provides a comprehensive overview of how vector databases, a specialized form of database optimized for vector similarity search, can enhance various generative AI applications.
The first part of the book introduces the fundamentals of vector databases, including key concepts such as vector indexing, similarity search algorithms, and performance optimizations. Readers are guided through the architecture and functionality of vector databases, with a focus on how they differ from traditional relational databases and their suitability for handling high-dimensional data.
In the second part, the book delves into the application of vector databases in generative AI. It explores how vector databases can be leveraged to store and retrieve large collections of high-dimensional vectors, which are prevalent in generative AI tasks such as natural language processing, computer vision, and recommender systems. Through real-world examples and case studies, the book demonstrates how vector databases can accelerate the training and inference processes of generative AI models by efficiently managing vector representations of data points.
Moreover, the book addresses the challenges and considerations involved in integrating vector databases with generative AI frameworks and platforms. It discusses topics such as data preprocessing, indexing strategies, distributed computing, and scalability, providing practical guidance for architects and developers looking to deploy vector databases in their generative AI pipelines.
Throughout the book, the authors highlight the synergies between vector databases and generative AI, showcasing how the combination of these technologies can enable breakthroughs in applications such as content generation, personalized recommendations, and data synthesis. By offering both theoretical insights and hands-on implementation techniques, "Vector Databases for Generative AI Applications" serves as a valuable resource for researchers, practitioners, and enthusiasts seeking to harness the power of vector databases to drive innovation in generative AI.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
- ÉditeurIndependently published
- Date d'édition2024
- ISBN 13 9798324701420
- ReliureBroché
- Langueanglais
- Nombre de pages33
- Coordonnées du fabricantnon disponible
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Paperback. Etat : new. Paperback. "Vector Databases for Generative AI Applications" explores the intersection of two cutting-edge fields: vector databases and generative artificial intelligence (AI). The book provides a comprehensive overview of how vector databases, a specialized form of database optimized for vector similarity search, can enhance various generative AI applications. The first part of the book introduces the fundamentals of vector databases, including key concepts such as vector indexing, similarity search algorithms, and performance optimizations. Readers are guided through the architecture and functionality of vector databases, with a focus on how they differ from traditional relational databases and their suitability for handling high-dimensional data. In the second part, the book delves into the application of vector databases in generative AI. It explores how vector databases can be leveraged to store and retrieve large collections of high-dimensional vectors, which are prevalent in generative AI tasks such as natural language processing, computer vision, and recommender systems. Through real-world examples and case studies, the book demonstrates how vector databases can accelerate the training and inference processes of generative AI models by efficiently managing vector representations of data points. Moreover, the book addresses the challenges and considerations involved in integrating vector databases with generative AI frameworks and platforms. It discusses topics such as data preprocessing, indexing strategies, distributed computing, and scalability, providing practical guidance for architects and developers looking to deploy vector databases in their generative AI pipelines. Throughout the book, the authors highlight the synergies between vector databases and generative AI, showcasing how the combination of these technologies can enable breakthroughs in applications such as content generation, personalized recommendations, and data synthesis. By offering both theoretical insights and hands-on implementation techniques, "Vector Databases for Generative AI Applications" serves as a valuable resource for researchers, practitioners, and enthusiasts seeking to harness the power of vector databases to drive innovation in generative AI. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798324701420
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Vector Databases for Generative AI Applications (Paperback)
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Paperback. Etat : new. Paperback. "Vector Databases for Generative AI Applications" explores the intersection of two cutting-edge fields: vector databases and generative artificial intelligence (AI). The book provides a comprehensive overview of how vector databases, a specialized form of database optimized for vector similarity search, can enhance various generative AI applications. The first part of the book introduces the fundamentals of vector databases, including key concepts such as vector indexing, similarity search algorithms, and performance optimizations. Readers are guided through the architecture and functionality of vector databases, with a focus on how they differ from traditional relational databases and their suitability for handling high-dimensional data. In the second part, the book delves into the application of vector databases in generative AI. It explores how vector databases can be leveraged to store and retrieve large collections of high-dimensional vectors, which are prevalent in generative AI tasks such as natural language processing, computer vision, and recommender systems. Through real-world examples and case studies, the book demonstrates how vector databases can accelerate the training and inference processes of generative AI models by efficiently managing vector representations of data points. Moreover, the book addresses the challenges and considerations involved in integrating vector databases with generative AI frameworks and platforms. It discusses topics such as data preprocessing, indexing strategies, distributed computing, and scalability, providing practical guidance for architects and developers looking to deploy vector databases in their generative AI pipelines. Throughout the book, the authors highlight the synergies between vector databases and generative AI, showcasing how the combination of these technologies can enable breakthroughs in applications such as content generation, personalized recommendations, and data synthesis. By offering both theoretical insights and hands-on implementation techniques, "Vector Databases for Generative AI Applications" serves as a valuable resource for researchers, practitioners, and enthusiasts seeking to harness the power of vector databases to drive innovation in generative AI. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798324701420
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