The LLM Production : From Prototype to Petascale

De: Ajit Singh

Narrado por: Virtual Voice

This book is not about building your first chatbot. It's about taking that chatbot and making it reliable, scalable, and observable for millions of users. It's the definitive guide to MLOps for Language Models (LLMOps), covering the entire production lifecycle.


Why It's a Market Gap:

Most books stop at the response = chain.invoke() step. The immense complexity of logging, monitoring, CI/CD, A/B testing, and managing regressions in LLM systems is a huge pain point with almost no consolidated literature.


Key Features:

1. End-to-End Lifecycle Coverage: From initial system design and prompt engineering to CI/CD, A/B testing, and long-term maintenance.
2. Hands-On and Practical: Packed with working code examples, practical tutorials, and step-by-step guides you can run on your own machine.
3. Vendor-Agnostic Principles: While we use specific tools for examples (e.g., OpenAI, LangChain, Prometheus), the underlying principles of LLMOps taught are universal and can be applied to any model provider or framework.
4. Beginner to Advanced: The book is structured to guide a student or junior engineer from foundational concepts to advanced architectural patterns, while also serving as a valuable reference for experienced MLOps and DevOps professionals.


To Whom This Book Is For:

This book is written for the builders—the individuals and teams tasked with turning the promise of LLMs into production reality.

1. B.Tech/M.Tech Computer Science Students: An ideal textbook that provides the essential, practical skills needed for a top-tier career in AI/ML engineering, aligning perfectly with modern, project-based curricula.
2. DevOps and SRE Professionals: Your guide to understanding the unique challenges of deploying and maintaining non-deterministic, stateful AI systems.
3. MLOps Engineers: A focused, deep dive into the emerging specialty of LLMOps, covering the new tools and workflows specific to language models.
4. Senior Software Engineers & Architects: The knowledge you need to design and lead the development of robust, scalable, and cost-effective LLM-powered features and products.
5. AI/ML Team Leads and Managers: A comprehensive overview of the production lifecycle to help you plan projects, allocate resources, and set your team up for success.