{"product_id":"intelligent-systems-engineering-with-ai-amp-cloud","title":"Intelligent Systems Engineering with AI \u0026 Cloud","description":"\u003cp\u003e\u003cspan\u003eIntelligent Systems Engineering with AI \u0026amp; Cloud is an Academic Edition that presents a structured, systems-oriented approach to designing, developing, deploying, operating, and governing modern intelligent systems. Rather than treating artificial intelligence as an isolated model-building activity, the book integrates AI, machine learning, cloud computing, data engineering, software architecture, security, reliability, MLOps, responsible AI, and operational engineering into a complete intelligent-system life cycle. Its combination of conceptual foundations, architecture-oriented explanations, applied scenarios, review questions, practical exercises, case studies, and a capstone project makes it suitable for both academic learning and professional reference.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eIt will enable you to:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand intelligent systems as integrated combinations of data, models, software, cloud infrastructure, security, monitoring, and governance.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eBuild foundations in artificial intelligence, machine learning, deep learning, generative AI, foundation models, and large language models.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand cloud computing, distributed systems, containerisation, Kubernetes, microservices, serverless computing, and cloud-native architecture.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDesign reliable data pipelines using batch and stream processing, ETL\/ELT, data lakes, warehouses, lakehouses, and distributed processing.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eEngineer machine learning systems across training, validation, experiment tracking, feature management, deployment, monitoring, and retraining.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eApply MLOps practices including CI\/CD, continuous training, model registries, canary, blue-green, shadow, and A\/B deployment strategies.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDesign generative AI applications using embeddings, vector databases, Retrieval-Augmented Generation, prompt engineering, and AI agents.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eEvaluate model-serving systems for latency, throughput, availability, scalability, hardware efficiency, and cost.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAddress cybersecurity, zero-trust architecture, AI-specific threats, reliability, observability, resilience, and incident response.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eApply responsible AI principles covering fairness, explainability, privacy, human oversight, governance, compliance, and environmental impact.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDesign an end-to-end intelligent system through structured architecture, security, monitoring, evaluation, and responsible-AI requirements.\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eWho should read?\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eUndergraduate students studying AI, computer science, cloud computing, data science, or software engineering.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePostgraduate students exploring intelligent systems and production AI engineering.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSoftware and systems engineers building AI-enabled applications.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eData scientists and machine learning engineers moving models into production.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCloud architects and cloud engineers designing scalable intelligent platforms.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMLOps, DevOps, data engineering, and platform engineering professionals.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTechnology leaders and project teams responsible for enterprise AI adoption, governance, and risk.\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Natals Publication","offers":[{"title":"Default Title","offer_id":67621228249241,"sku":"NP-ISEAC-SK-2026-PB","price":430.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/4913\/0649\/files\/IntelligentSystemsEngineering.png?v=1791301156","url":"https:\/\/natals.in\/products\/intelligent-systems-engineering-with-ai-amp-cloud","provider":"Natals Publication","version":"1.0","type":"link"}