Advanced Applied AI Engineering

ADVANCED APPLIED AI ENGINEERING

Build AI systems that go beyond impressive demonstrations-and stand up to real-world engineering challenges.

Artificial intelligence is transforming industries, but building a successful AI application requires far more than selecting a powerful model or writing effective prompts.

Reliable AI engineering demands structured data, measurable evaluation, secure architectures, reproducible experiments, operational monitoring, and responsible human oversight.

Advanced Applied AI Engineering provides a comprehensive, practice-oriented approach to designing, evaluating, and managing modern AI systems.

Across 12 detailed chapters, readers explore the engineering principles behind machine learning, large language models, retrieval-augmented generation, intelligent workflows, and production-oriented AI applications.

WHAT YOU WILL LEARN

• AI problem framing, system requirements, and evaluation design
• Data engineering, data quality, and prevention of machine learning data leakage
• Supervised machine learning and model performance analysis
• Responsible AI, model documentation, and risk management
• Large Language Models (LLMs), prompt contracts, and structured outputs
• MLOps, reproducibility, and AI system observability
• Retrieval-Augmented Generation (RAG) and knowledge-grounded AI
• Multimodal AI processing and information extraction
• Intelligent workflow orchestration and human-in-the-loop systems
• AI evaluation, performance measurement, latency, and cost optimization
• LLM security, robustness testing, and failure analysis
• AI system handover, documentation, and professional engineering judgment

FROM THEORY TO PRACTICAL AI ENGINEERING

The book follows Atlas Service Desk AI, a fictional bilingual service environment used to demonstrate how AI components can be designed, tested, evaluated, and integrated into controlled workflows.

Through engineering examples, Python implementations, guided laboratory activities, exercises, and assessment questions, readers develop a systematic understanding of how intelligent systems operate-and how they fail.

Particular attention is given to security, responsible deployment, evidence-based decision-making, and the practical limitations of modern AI technologies.

WHO SHOULD READ THIS BOOK?

• AI and machine learning engineers
• Software developers building AI-powered applications
• Data scientists and technical analysts
• IT professionals transitioning into AI engineering
• Advanced undergraduate and graduate students
• Researchers and technology professionals interested in production-oriented AI systems

ENGINEER INTELLIGENCE WITH CONFIDENCE

Move beyond isolated models and disconnected experiments.

Learn to design AI solutions that are measurable, reproducible, secure, maintainable, and aligned with real operational requirements.

Advanced Applied AI Engineering - A Practical Guide to Building Reliable AI Systems.