Ulteriori informazioni
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
Info autore
Abi Aryan is the founder of Abide AI (www.abideai.com) and a machine learning research engineer with nearly a decade of experience building production-level ML systems. A mathematician by training, she previously served as a visiting research scholar at the Cognitive Systems Lab at UCLA, under Dr. Judea Pearl, where she focused on developing intelligent agents.
Abi has authored research papers in AutoML, multi-agent systems, and large language models, and actively reviews for leading research conferences and workshops, including NeurIPS, ACL (Association for Computational Linguistics), EMNLP (Empirical Methods in Natural Language Processing), and AABI (Advances in Approximate Bayesian Inference). She is currently advancing research in reflective intelligence in AI agents, distributed self-healing protocols for multi-agent systems, and GPU engineering for very large-scale AI systems.
Riassunto
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.
