About this book:

*Edge AI and Real-Time Systems* provides a comprehensive technical framework for designing and deploying machine learning models in latency-sensitive environments where decisions must be made in milliseconds. The book establishes that successful edge deployment is a systems-level challenge, requiring a delicate balance between model excellence and hardware constraints. It begins by grounding the reader in real-time fundamentals—deadlines, jitter, and determinism—and explains how to characterize workloads from initial sensing to final actuation. By focusing on the accuracy–latency–power frontier, the text guides engineers through picking the right silicon, whether it be CPUs, GPUs, NPUs, FPGAs, or microcontrollers.

A significant portion of the book is dedicated to the "distillation" of AI models for resource-constrained hardware. It details essential compression techniques, including quantization, pruning, and knowledge distillation, alongside the use of specialized compilers and runtimes like TVM, TensorRT, and OpenVINO. The book emphasizes that a model's performance is inextricably linked to its operating environment, necessitating the use of real-time operating systems (RTOS) or PREEMPT_RT Linux, as well as robust resource management and scheduling to maintain consistent inference under heavy workloads.

Beyond individual devices, the text addresses the complexities of managing distributed "fleets" of intelligent agents. It covers edge-specific orchestration using lightweight Kubernetes (K3s), resilient data streaming via Kafka or MQTT, and the critical need for observability through tracing and profiling. Security and privacy are treated as foundational requirements rather than features, with in-depth explorations of hardware roots of trust (TPM/TEE), remote attestation, and privacy-preserving methods like federated learning and differential privacy. The book also introduces "designing for failure," ensuring that systems can operate offline or in degraded modes when networks or sensors fail.

In its concluding chapters, the book provides "domain playbooks" for industries such as robotics, automotive, healthcare, and Industrial IoT, illustrating how these theoretical principles are applied in practice. It outlines the full lifecycle of an edge AI product, from continuous delivery and over-the-air (OTA) updates to monitoring and retraining for model drift. Finally, it ties technical decisions to economic reality, providing frameworks for calculating Total Cost of Ownership (TCO) and Return on Investment (ROI), culminating in reference architectures that serve as blueprints for building reliable, secure, and intelligent real-time systems.

What You'll Find Inside:

• Edge AI is driven by latency sensitivity, privacy needs, and resilience requirements, enabling real-time decisions where data is generated.
• Real-time system fundamentals—deadlines, jitter, determinism, and scheduling—are essential for consistent inference under constrained resources.
• Model compression techniques such as quantization, pruning, sparsity, and distillation reduce model size and power while preserving accuracy for edge deployment.
• Hardware selection involves trade-offs among CPUs, GPUs, NPUs, FPGAs, and MCUs based on latency, power, cost, and ecosystem support.
• Orchestration, observability, security, and lifecycle management (including K3s, OTA updates, drift detection, and resilient design) are critical for scalable, trustworthy edge AI fleets.

Who's It For:

This book is intended for embedded systems engineers, machine learning practitioners, and product technical leads who need to design, optimize, and deploy latency-sensitive AI applications on edge devices. It assumes familiarity with AI concepts and provides the systems-level knowledge required to meet real-time deadlines, manage hardware-software trade-offs, ensure security and privacy, and operate fleets reliably in disconnected or adverse environments.