Overview

What if the next revolution in artificial intelligence isn't about bigger models, but smarter systems that know where to think? From Edge to Cloud: Architecting Hybrid AI Systems with Small and Large Language Models for Real-World Scalability explores how AI is evolving beyond the data center to operate seamlessly across devices, networks, and the cloud. Geoffrey Allen presents a clear, practical guide to designing hybrid AI architectures that balance speed, cost, privacy, and power, bridging the gap between small, efficient models at the edge and large, intelligent models in the cloud. In a world of connected sensors, autonomous machines, and instant data, this book shows how hybrid intelligence is reshaping industries from manufacturing and healthcare to finance and smart cities. Readers will learn how to: Build edge-first, cloud-first, and collaborative architectures that meet real-world latency and privacy demands. Coordinate Small Language Models (SLMs) and Large Language Models (LLMs) through dynamic routing, co-inference, and adaptive workflows. Manage data orchestration, scalability, and system performance across distributed environments. Apply MLOps practices for hybrid deployment, monitoring, and reliability at scale. Explore industry case studies that bring hybrid AI to life in robotics, medical systems, and urban infrastructure. Understand the future of AI with TinyML, neuromorphic chips, and multi-modal ecosystems. What sets this book apart is its balance of engineering depth and strategic clarity. It's written for those who build and those who plan, including software engineers, data scientists, system architects, and decision-makers who want to create AI systems that are fast, secure, and sustainable. Whether you're architecting a smart factory, designing an AI-powered device, or leading digital transformation, this is your guide to scaling intelligence across the edge-cloud continuum. Ready to design the next generation of intelligent systems? Get your copy of From Edge to Cloud and start building AI that truly scales, everywhere it's needed.

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