E-Books →MLOps and Infrastructure Technologies

Key Features:
1. Beginner to Advanced Progression: The book caters to a wide audience, starting with fundamental concepts for beginners and gradually introducing advanced topics like inference optimization engines, LLMOps, and cloud infrastructure, making it suitable for both undergraduate and postgraduate students.
2. Hands-On and Practical-First Approach: Learning is reinforced through hands-on exercises at the end of key chapters. The book is packed with code examples and practicals that readers can execute to gain real-world experience.
3. Real-World Case Studies: Theoretical concepts are contextualized with case studies from the industry, illustrating how MLOps principles are applied to solve real business problems.
4. Simplest Language and Clear Examples: Complex topics are broken down into simple, easy-to-understand language, using relatable analogies and the simplest possible examples to explain core concepts.
5. Coverage of Latest Trends: Stay ahead of the curve with dedicated chapters and sections on the most current and valuable topics, including LLMOps, on-device AI, model quantization, and responsible AI.
6. Complete End-to-End Capstone Project: The final chapter guides the reader through building a complete MLOps project from scratch, integrating the tools and techniques learned throughout the book into a single, cohesive system.
7. NEP 2020 and Global Syllabus Aligned: The content is carefully curated to align with the skill-based, multidisciplinary, and practical learning objectives of India's NEP 2020, the AICTE model curriculum, and the syllabi of leading international universities.
To Whom This Book Is For:
This book is an essential resource for a diverse range of learners and professionals:
1. B.Tech and M.Tech Students: Primarily aimed at students in Computer Science, Information Technology, AI & Machine Learning, and related disciplines who want to build a strong foundation in production ML.
2. Aspiring MLOps and ML Engineers: Individuals looking to start a career in the specialized and high-demand field of MLOps.
3. Data Scientists and ML Practitioners: Professionals who can build models but want to learn how to deploy, scale, and manage them effectively in a production environment.
4. Software and DevOps Engineers: Engineers who are transitioning into the ML space and want to understand how to apply DevOps principles to the unique challenges of the machine learning lifecycle.
5. Academics and Instructors: Educators seeking a comprehensive, practical, and syllabus-compliant textbook for their courses on MLOps, Applied ML, or AI Systems.