LLMOps: Managing Large Language Models in Production by Abi Aryan

LLMOps: Managing Large Language Models in Production by Abi Aryan

The central thesis of LLMOps: Managing Large Language Models in Production is that the real challenge of generative AI isn't building a wrapper application or writing a clever system prompt—it is maintaining system safety, cost efficiency, and reliability after the model encounters real users and real money. Aryan analyzes the critical failure states that emerge post-deployment, such as data drift, sudden latency spikes, prompt regressions, hidden token costs, and complex security risks like prompt injection and jailbreaking.

Rather than focusing on surface-level playground fine-tuning, Aryan dives into full-lifecycle system architecture. She explores the unique characteristics of encoder-only, decoder-only, and state-space model structures, moving step-by-step through data lifecycle management, API-first deployment strategies, and hardware resource constraints. By providing clear frameworks for evaluating outputs using specialized "LLM-as-a-Judge" metrics, building resilient retrieval-augmented generation (RAG) pipelines, and conducting rigorous security audits, this book establishes the foundational engineering principles required to run complex multi-agent setups smoothly at scale.

As regional software development groups, tech enterprise houses, and modern IT service teams move fast to deploy generative artificial intelligence systems for corporate clients, engineering teams are encountering severe operational barriers. While developers can easily build basic, front-end chat interfaces using public APIs, moving those configurations into massive, multi-turn corporate systems regularly leads to unpredictable software bugs, high token expenses, and dangerous data exposure.

LLMOps: Managing Large Language Models in Production provides the exact, low-level operational manual our technical sector needs. Abi Aryan perfectly translates her deep history in machine learning research into an incredibly detailed, code-forward guide. By packing every chapter with clear architectural diagrams, structured tracing routines, and practical security audit templates, this O'Reilly volume equips platform engineers, machine learning operations professionals, and systems architects with the precise tools required to deploy safe, reliable, and highly scalable applications. It is a mandatory addition to any enterprise developer's library.

Language: English.

Genre: Artificial Intelligence Security.

Binding: সেলাই করা বাইন্ডিং

Quality: Premium Quality Books.

Printing: High Quality Printing.

Paper: Eye Friendly paper (Cream White)

Cover: Matt cover (Paperback).