Intelligent Systems Engineering with AI & Cloud

Intelligent Systems Engineering with AI & 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.

It will enable you to:

• Understand intelligent systems as integrated combinations of data, models, software, cloud infrastructure, security, monitoring, and governance.
• Build foundations in artificial intelligence, machine learning, deep learning, generative AI, foundation models, and large language models.
• Understand cloud computing, distributed systems, containerisation, Kubernetes, microservices, serverless computing, and cloud-native architecture.
• Design reliable data pipelines using batch and stream processing, ETL/ELT, data lakes, warehouses, lakehouses, and distributed processing.
• Engineer machine learning systems across training, validation, experiment tracking, feature management, deployment, monitoring, and retraining.
• Apply MLOps practices including CI/CD, continuous training, model registries, canary, blue-green, shadow, and A/B deployment strategies.
• Design generative AI applications using embeddings, vector databases, Retrieval-Augmented Generation, prompt engineering, and AI agents.
• Evaluate model-serving systems for latency, throughput, availability, scalability, hardware efficiency, and cost.
• Address cybersecurity, zero-trust architecture, AI-specific threats, reliability, observability, resilience, and incident response.
• Apply responsible AI principles covering fairness, explainability, privacy, human oversight, governance, compliance, and environmental impact.
• Design an end-to-end intelligent system through structured architecture, security, monitoring, evaluation, and responsible-AI requirements.

Who should read?

• Undergraduate students studying AI, computer science, cloud computing, data science, or software engineering.
• Postgraduate students exploring intelligent systems and production AI engineering.
• Software and systems engineers building AI-enabled applications.
• Data scientists and machine learning engineers moving models into production.
• Cloud architects and cloud engineers designing scalable intelligent platforms.
• MLOps, DevOps, data engineering, and platform engineering professionals.
• Technology leaders and project teams responsible for enterprise AI adoption, governance, and risk.