Machine Learning for Edge Computing 1st Edition

Capture the future of distributed intelligence with Machine Learning for Edge Computing, 1st Edition by Amitoj Singh. This authoritative guide hooks you immediately by addressing the urgent challenges of running smart models on resource-constrained edge devices—low latency, limited memory, privacy, and intermittent connectivity.

Inside, you’ll find clear, practical explanations of core techniques used to design, optimize, and deploy machine learning at the edge. Topics include model compression and quantization, on-device inference, edge-tailored architectures, energy-aware scheduling, and privacy-preserving approaches like federated learning. Real-world examples and deployment strategies make complex concepts accessible to engineers, data scientists, and technical managers who need results in production environments.

What sets this book apart is its applied focus: learn how to balance accuracy with efficiency, select the right hardware-software co-design, and scale solutions across diverse settings—from industrial IoT and smart-city sensors to autonomous systems and mobile applications. The writing is concise, modern, and geared toward practitioners and advanced students globally, making it a valuable resource for teams in North America, Europe, Asia, and beyond.

Whether you’re building prototypes or optimizing large-scale deployments, this book equips you with the tools and frameworks to accelerate edge ML projects with confidence. Add Machine Learning for Edge Computing by Amitoj Singh to your professional library today and transform the way you design intelligent, on-device systems.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.