Overview

Modern AI is no longer defined only by scale. Real-world systems demand speed, efficiency, privacy, and cost control especially when intelligence must run on devices, at the edge, or under tight infrastructure budgets. This book is written for that reality. Small Language Models for Modern AI: On-Device AI, Edge Intelligence, and Cost-Efficient Deployment is a practical, systems-focused guide to building, optimizing, deploying, and maintaining compact language models that deliver real value in production. It shows how to move beyond cloud-heavy, GPU-dependent architectures and design AI systems that are fast, reliable, and sustainable. Rather than treating small models as simplified versions of large ones, this book presents them as first-class engineering tools. You will learn how and why Small Language Models succeed in environments where latency matters, privacy is critical, and resources are limited. Every concept is grounded in deployment realities, not academic abstraction. Inside the book, you will learn how to: - Understand what truly defines a Small Language Model and when it is the right choice - Design efficient transformer architectures optimized for constrained environments - Apply quantization, pruning, distillation, and hardware-aware optimization correctly - Train and fine-tune models using high-quality, task-aligned datasets - Deploy models on-device, at the edge, and in hybrid cloud systems - Build hybrid architectures that combine small and large models intelligently - Measure accuracy, latency, memory usage, and energy consumption in production - Implement observability, monitoring, and safe failure handling - Design systems that respect privacy, security, and regulatory requirements - Learn from real production deployments across multiple industries The book avoids hype and focuses on engineering discipline. It emphasizes clear task definition, constrained outputs, predictable behavior, and measurable outcomes. Whether you are building internal enterprise tools, consumer applications, edge systems, or privacy-sensitive platforms, the techniques in this book apply directly. This book is ideal for: - Machine learning engineers and AI practitioners - Backend and systems engineers integrating AI into products - Mobile, edge, and embedded developers - Technical leaders making cost and architecture decisions - Teams moving AI from prototype to production If you are tired of solutions that are expensive to run, difficult to control, or hard to deploy outside the cloud, this book provides a better path forward. Build AI systems that are fast, efficient, private, and production-ready. Get your copy today and start designing intelligence that fits the real world.

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