Cloud Innovation Scaling With Vectors And Llms
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Cloud Innovation: Scaling with Vectors and LLMs
Author: Sayantan Bhattacharyya
language: en
Publisher: Libertatem Media Private Limited
Release Date: 2024-09-11
Cloud-native architectures have become the foundation for modern enterprises seeking agility, scalability, and efficiency. Cloud Innovation: Scaling with Vectors and LLMs is a definitive guide for IT leaders, solution architects, and senior managers aiming to integrate advanced technologies like vector databases and Large Language Models (LLMs) into scalable cloud systems. This book explores the principles of cloud-native systems, providing actionable insights into designing elastic and resilient architectures. Readers will learn how to leverage vector databases for high-dimensional data management and harness the power of LLMs for natural language understanding and generation. From optimizing real-time data ingestion to implementing retrieval-augmented generation (RAG) systems, the book offers practical strategies for building future-ready applications. Key topics include the integration of LLMs with vector databases, techniques for elastic scaling, cost optimization strategies, and designing systems for high availability and disaster recovery. Real-world case studies demonstrate how leading enterprises have successfully deployed these technologies to drive innovation, enhance operational efficiency, and meet evolving customer needs. Packed with insights on security, compliance, and performance optimization, Cloud Innovation also examines emerging trends in cloud computing and AI, preparing professionals to navigate the next wave of digital transformation. Whether you ' re modernizing legacy systems or pioneering AI-driven solutions, this book equips you with the tools to scale efficiently, innovate boldly, and lead in the rapidly evolving cloud ecosystem.
Vector Databases Unleashed: Isolating Data in Multi-Tenant LLM Systems
Author: Jawaharbabu Jeyaraman
language: en
Publisher: Libertatem Media Private Limited
Release Date: 2025-01-08
In the era of AI-driven innovation, multi-tenant architectures leveraging Large Language Models (LLMs) and vector databases are redefining scalability and efficiency. Vector Databases Unleashed: Isolating Data in Multi-Tenant LLM Systems is the definitive guide for senior IT professionals and data architects looking to master the intricacies of data isolation, scalability, and security in shared environments. This book delves into the foundational principles of multi-tenant systems, emphasizing robust data isolation techniques to ensure privacy and compliance in shared infrastructures. It explores the power of vector databases, their seamless integration with LLMs, and how they unlock the potential for real-time data retrieval and advanced AI applications. Readers will discover practical strategies for embedding isolation techniques, managing tenant access, and implementing scalable architectures that balance performance with security. Packed with actionable insights, the book offers detailed frameworks for designing and deploying multi- tenant LLM systems, complete with real-world case studies that highlight successful implementations and lessons learned. From physical and logical isolation models to advanced encryption and containerization, it provides a roadmap for navigating complex challenges in data-driven enterprises. Vector Databases Unleashed is essential for IT leaders, data scientists, and engineers aiming to harness the power of AI while maintaining stringent security standards in multi-tenant ecosystems. Prepare to transform your understanding of modern data architecture and drive innovation with confidence in this groundbreaking field.
Economics of Grids, Clouds, Systems, and Services
This book constitutes the refereed proceedings of the 20th International Conference on Economics of Grids, Clouds, Systems, and Services, GECON 2024, held in Rome, Italy, during September 26–27, 2024. The 12 full papers and 10 short papers presented in this book were carefully reviewed and selected from 37 submissions. They focus on topics such as: Function as a Service: Resource Management and QoS; Cloud Business Models, Pricing, Trading, Network Neutrality; Edge Computing and Energy Awareness; AI/Forecasting/Prediction Sales; and Resource Management in Cloud Applications: Simulation, Streaming Processing and Workflows.