AI in Automation & Smart Systems

The foundational philosophy of AI in Automation & Smart Systems can be summarized in a single directive: Implementation over Theory. >Key Features 1. Complete End-to-End Pipeline: Unlike books that only cover model training, this book covers the complete lifecycle: Design, Build, Setup, Implementation, Deployment, and Production. 2. Industry-Relevant Frameworks: Focuses on the latest, updated tools used in current smart systems, including Docker, Kubernetes, Edge AI platforms, and modern Python-based AI libraries. 3. Architectural Focus: Detailed explanations of system components, elements, services, and how they function together in a unified smart architecture. 4. Real-Life Case Studies: Each chapter contains practical examples mirroring real-world industry requirements (e.g., smart manufacturing defect detection, automated smart home hubs, predictive maintenance pipelines). 5. Hardware & Software Integration: Explores the intersection of AI with IoT, edge devices, and physical automation systems, explaining the precise mode of operations for hardware-software handshakes. 6. Live DIY Capstone Project: The final chapter culminates in a complete, working, deployable project with line-by-line code explanations, allowing the reader to build a fully functional smart system from scratch. Key Takeaways By completing this book, readers will extract the following core competencies: 1. Mastery of AI Foundations: A clear understanding of AI classifications, histories, versions, and exactly how they compare and function within industrial automation. 2. Practical Development Skills: The ability to write clean, functional code for machine learning, deep learning, computer vision, and natural language processing applications. 3. Architectural Design: The knowledge to design scalable frameworks and model architectures for IoT and edge computing environments. 4. Deployment Expertise: The capability to set up environments, containerize applications, and push AI models into live, real-world production environments securely. 5. Problem Solving: The capacity to look at a real-life industry requirement, select the appropriate AI service/component, and build a customized, automated smart solution from scratch. 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!