Cloud Computing for AI/ML

"Cloud Computing for AI/ML" is a comprehensive, practical, and project-oriented guide designed to equip students and professionals with the skills needed to build, deploy, and manage artificial intelligence and machine learning applications using cloud platforms. This book is meticulously crafted to serve as a primary textbook for undergraduate (B.Tech) and postgraduate (M.Tech) courses in Computer Science and related fields, aligning with the curricula of Indian universities (NEP 2020, AICTE) and international academic institutions.


Philosophy

The core philosophy of this book is "Implementation over Theory." While foundational concepts are explained clearly and concisely, the primary focus is on practical application. I worked on the principle that true understanding comes from building. The text systematically dismantles the complexity of deploying AI/ML systems by using a step-by-step, hands-on approach. It treats cloud platforms not as abstract concepts but as a tangible toolkit for creating real-world solutions. Every chapter is a building block, designed to impart a specific, marketable skill that contributes to the reader's ability to develop end-to-end AI/ML systems.


Key Features

1. Practical Orientation: Over 70% of the content is focused on implementation, with detailed tutorials, code snippets, and configuration guides for major cloud platforms (AWS, Azure, GCP).

2. NEP 2020 & AICTE Compliant: The structure and content are aligned with the skill-based, multidisciplinary, and practical learning objectives outlined in India's National Education Policy 2020 and the AICTE curriculum.

3. Globally Relevant: While compliant with Indian standards, the curriculum covers universally applicable concepts and tools, making it suitable for any university computer science program worldwide.

4. Beginner to Advanced: The book starts with the absolute basics, making it accessible to those new to cloud computing. However, it gradually introduces advanced topics like MLOps, serverless AI, and cost optimization, providing a clear learning path for advanced learners.

5. End-to-End Project: The final chapter offers a complete walkthrough of a live project, from data ingestion and model training to API deployment and monitoring, providing an invaluable portfolio piece for students.


Key Takeaways

Upon completing this book, the reader will be able to:

1. Design AI/ML Architectures on the Cloud: Understand the trade-offs and choose the right cloud services for different AI/ML tasks.

2. Manage the AI/ML Data Lifecycle: Implement robust data ingestion, storage, and processing pipelines using cloud-native tools.

3. Train and Evaluate Models at Scale: Utilize managed cloud services for efficient model training, hyperparameter tuning, and evaluation.

4. Deploy Models as Production-Ready Services: Master various deployment strategies, including real-time APIs, batch processing, and serverless functions.

5. Implement MLOps Practices: Build automated CI/CD pipelines for models, ensuring reproducibility, monitoring, and governance.

6. Build a Complete End-to-End Solution: Successfully complete a capstone project that demonstrates mastery of the entire AI/ML development and deployment lifecycle on the cloud.

Disclaimer: Earnest request from the Author.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

Thank you for your kind consideration!