Tiny Machine Learning (TinyML) is an emerging field of artificial intelligence that allows machine learning models to be executed directly on resource-constrained devices like microcontrollers, sensors and embedded systems. Unlike traditional cloud-based approaches, TinyML brings real-time intelligence to the edge with minimal computational and energy requirements. This capability makes TinyML a key enabler of sustainable edge AI by reducing latency, enhancing data privacy through local processing, and minimizing dependence on centralized cloud infrastructure.
This book explores how TinyML is transforming sectors such as agriculture, environmental science, healthcare, urban planning, and industrial automation by enabling intelligent data processing without reliance on cloud infrastructures. These edge-centric applications dramatically reduce energy consumption and latency, contributing to both environmental sustainability and enhanced user experience. Bridging theory and practice, this volume compiles cutting-edge research, methodologies, and case studies that highlight the design, deployment, and optimization of TinyML systems. Emphasis is placed on low-power hardware, domain-specific constraints, privacy-preserving AI, and ethical considerations.
Intended for academic researchers, engineers, data scientists, and graduate students, this book offers foundational insights and advanced approaches that are crucial for developing sustainable AI models at the edge.