AI-Driven Aerospace and Scalable Cloud Systems : Bridging Research and Engineering Practice: Bridging Research and Engineering Practice

By Sen, Arnab

Book Id: WPLBN0100750972
Format Type: PDF (eBook)
File Size: 36.22 MB.
Reproduction Date: 11/28/2025

Title:	AI-Driven Aerospace and Scalable Cloud Systems : Bridging Research and Engineering Practice: Bridging Research and Engineering Practice
Author:	Sen, Arnab
Volume:
Language:	English
Subject:	Non Fiction, Technology
Collections:	Authors Community, Technology
Historic Publication Date:	2025
Publisher:	Arnab Sen

Member Page:	Arnab Sen
Citation APA MLA Chicago Sen, A. (2025). AI-Driven Aerospace and Scalable Cloud Systems : Bridging Research and Engineering Practice. Retrieved from https://www.self.gutenberg.org/

Description
AI-Driven Aerospace and Scalable Cloud Systems is a forward-looking guide for engineers, researchers, and technology leaders. It reveals how drones, autonomous systems, and cloud-native architectures are converging to define the future of intelligent flight. With practical case studies, recruiter-focused summaries, and deep dives into algorithms, protocols, and architectures, the book equips readers to navigate both academic and industry challenges. Whether you’re fascinated by UAV swarm intelligence, predictive maintenance, or generative AI in safety-critical domains, this book offers a roadmap to mastering aerospace innovation in the cloud era.

Summary
This book explores the fusion of artificial intelligence, aerospace engineering, and scalable cloud systems. It traces the evolution from deterministic control models to probabilistic AI, showing how drones, autonomous flight systems, and digital twins are reshaping aviation. Each chapter bridges theory with engineering practice—covering machine learning algorithms, swarm intelligence, data pipelines, observability, MLOps, and edge computing—while highlighting recruiter-friendly insights for professionals aiming to work at the intersection of aerospace and cloud technology.

Excerpt
“Artificial Intelligence in aerospace does not replace control theory; it augments it. The transition involves moving from hard-coded rules to learned representations, specifically in areas where environmental variables are too chaotic for static modeling—such as gust mitigation in urban canyons or multi-object detection in diverse weather conditions.”

Table of Contents
- Foundations of AI in Aerospace Systems - Drone Technology and Autonomous Flight Systems - Cloud Infrastructure for Aerospace Applications - Data Pipeline Architecture - Observability and Monitoring in Mission-Critical Systems - Multi-Agent Systems and Swarm Intelligence - Generative AI and RAG Systems - Real-Time Processing and Stream Analytics - Predictive Maintenance Using AI and IoT - Security and Compliance in Aerospace Cloud - Cost Optimization (FinOps) - Multi-Cloud and Hybrid Deployment - AI Model Training and Deployment (MLOps) - Digital Twin Technologies - Edge Computing Hardware and Architecture - API Design and Microservices - DevOps and Infrastructure as Code (IaC) - Performance Engineering - Academic-Industry Knowledge Transfer - Leadership in Technology Transformation Conclusion: The Future of the Fusion