Hybrid Intelligence Engineering: Combining Small and Large Language Models to Build Secure, Scalable, and Affordable AI Solutions for Cloud and Edge - crafted for maximum engagement and visibility in the AI and technology categories: Build the future of AI-where small and large language models work together seamlessly across the cloud and edge. Hybrid Intelligence Engineering is your complete guide to creating next-generation AI systems that are faster, smarter, and more affordable. Learn how to combine lightweight edge models for instant local processing with powerful cloud-based LLMs for deep reasoning and decision-making. Through text-based flowcharts, tables, and hands-on projects, this book shows you exactly how to design, deploy, and optimize hybrid AI workflows that balance speed, security, and cost. From architecture blueprints to real-world case studies, you'll gain practical skills to engineer scalable and responsible AI solutions for any environment. You'll Learn How To: - Orchestrate small and large LLMs for hybrid performance - Secure distributed inference pipelines - Optimize latency, throughput, and cost - Deploy AI efficiently on both cloud and edge - Build hands-on projects using Python and FastAPI Perfect For: AI engineers, data scientists, developers, and innovators ready to master hybrid AI architectures and shape the next wave of intelligent systems.
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