Cloud Native AI

From Monolith to Micro-intelligence

"Cloud Native AI: From Monolith to Micro-intelligence" is a comprehensive, practical-first guide designed to bridge the critical gap between Artificial Intelligence development and modern Cloud Native operations. This textbook serves as both a foundational learning resource for students and a practical handbook for professionals, navigating the complete journey from a theoretical AI model to a fully operational, scalable, and resilient production service.


Philosophy

The core philosophy of this book is "learning by doing." We believe that true mastery of a technical subject like Cloud Native AI cannot be achieved through passive reading alone. It requires active engagement, experimentation, and building. The book is structured to demystify complex, interconnected technologies by breaking them down into logical, manageable components and immediately reinforcing concepts with hands-on labs and real-world examples. We move beyond the "what" and the "why" to focus intensely on the "how," empowering readers to build confidence and practical skills with every chapter.


Key Features

1. Globally Relevant Curriculum: By focusing on globally adopted, vendor-neutral technologies like Docker, Kubernetes, and TensorFlow, the content is fully compatible with the computer science syllabi of international universities.
2. Hands-On Practicals: Nearly every chapter includes practical labs and step-by-step tutorials that readers can execute on their own machines or cloud environments.
3. DIY Capstone Project: The final chapter is a comprehensive, end-to-end project to build a real-time sentiment analysis pipeline, integrating all the skills learned throughout the book into a single, impressive portfolio piece.
4. Focus on MLOps: A significant portion of the book is dedicated to the principles and practices of MLOps (Machine Learning Operations), a critical skill for modern AI professionals.


To Whom This Book Is For

1. Aspiring AI/ML Engineers: For those who know how to build models but want to learn how to deploy and manage them in production.
2. Data Scientists: For data scientists looking to understand the engineering side of their work and take ownership of the end-to-end model lifecycle.
3. Software & DevOps Engineers: For engineers who want to incorporate AI/ML capabilities into their applications and manage AI workloads using standard Cloud Native tools.
4. Cloud Architects: For architects designing scalable, resilient, and cost-effective infrastructure for modern AI applications.