The foundational philosophy of "Edge Computing With Agentic AI" is rooted in pragmatic engineering and actionable intelligence. The technology sector is currently saturated with high-level discourse about what AI "might" do in the future. However, industry engineers are tasked with what AI "must" do today. This book operates on the philosophy that true mastery of a technology is achieved only through building it. Therefore, I prioritized practical application over abstract academic theory. Key Features
1. Industry-Relevant Curriculum: Every chapter is aligned with current market trends and enterprise requirements. The focus is on solving real bottlenecks: latency, bandwidth costs, data privacy, and offline capabilities.
2. Comprehensive Lifecycle Coverage: The text covers the entire software development lifecycle (SDLC) for Edge AI. This includes design, architecture, model selection, framework integration, component setup, deployment, implementation, functioning, and final production MLOps.
3. Exhaustive Case Studies: Abstract concepts are grounded in reality through real-world case studies spanning smart manufacturing, autonomous vehicles, healthcare monitoring, and smart agriculture.
4. Structured Numbering: To ensure easy navigation and reference, all topics and subtopics are strictly numbered (e.g., 1.1, 1.2, 1.3), creating a highly organized reading experience.
5. DIY Capstone Project: A complete, working, industry-grade capstone project in Chapter 10 provides a definitive test of the reader's skills, complete with deployable code and step-by-step explanations.
Key Takeaways
Upon completing this book, readers will possess the ability to:
Comprehensively understand the history, evolution, classification, and precise need for Agentic AI at the Edge.
1. Design robust, scalable, and secure architectures tailored for resource-constrained edge environments.
2. Set up hardware and software frameworks from scratch, bypassing common configuration errors.
3. Optimize, compress, and deploy large AI models onto edge microprocessors without losing critical accuracy.
4. Develop autonomous agents capable of perceiving environments, utilizing tools, and executing complex, multi-step tasks locally.
5. Implement CI/CD pipelines and MLOps strategies specifically designed for fleet management of edge devices.
6. Ensure the security, monitoring, and continuous operation of edge deployments in production environments.
7. Build a complete, end-to-end working Edge AI application from the ground up, ready for real-world industry use.
Disclaimer: Earnest request from the Author.
Kindly go through the table of contents and refer kindle edition for a glance on the related contents.
Thank you for your kind consideration!
