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July 12, 2025, 9:58 a.m.
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SOURCE: Aryan A. LLMOps. Managing Large Language Models in Production 2025
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MEDIAINFO
Textbook in PDF format Here's the thing about Large Language Models: they don't play by the old rules. Traditional MLOps completely falls apart when you're dealing with GenAI. The model hallucinates, security assumptions crumble, monitoring breaks, and agents can't operate. Suddenly you're in uncharted territory. That's exactly why LLMOps has emerged as its own discipline. LLMOps: Managing Large Language Models in Production is your guide to actually running these systems when real users and real money are on the line. This book isn't about building cool demos. It's about keeping LLM systems running smoothly in the real world. Navigate the new roles and processes that LLM operations require Monitor LLM performance when traditional metrics don't tell the whole story Set up evaluations, governance, and security audits that actually matter for GenAI Wrangle the operational mess of agents, RAG systems, and evolving prompts Scale infrastructure without burning through your compute budget In traditional software development (or Software 2.0), you wouldn’t ask your lead developer to build and maintain your entire product. Software development engineers build, and reliability engineers maintain. Building and maintaining LLMs requires a similar separation of duties. In Software 3.0, LLM/AI engineers build and LLMOps engineers maintain! Although machine learning operations (MLOps) are foundational to LLMOps, the MLOps skills that engineers gain from working on structured data and discriminative models don’t fully translate to generative models. In short, I’m writing this book to help you appreciate the unique aspects of the full LLM-based application lifecycle, from data engineering to model deployment and API design to monitoring, security, and resource optimization. I want to give you a strong foundation for making decisions as you build, maintain, and optimize your LLM data, models, and applications
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