LLMOps Architecture Deep Dive | How Production LLM Systems Are Designed скачать в хорошем качестве

LLMOps Architecture Deep Dive | How Production LLM Systems Are Designed

Large language models are easy to integrate, but operating them reliably in production is a different challenge. In this video, I explain what LLMOps really means from a systems and platform engineering perspective, and how production AI systems are structured across multiple layers including orchestration, model abstraction, retrieval, data ingestion, observability, governance, and cost control. This video focuses on architecture and operating principles rather than tools or code. The goal is to help engineers understand how to design, run, and maintain LLM-powered systems that are reliable, observable, and scalable in real environments. I also connect these ideas to real enterprise patterns and briefly map where cloud platforms like AWS typically fit into the overall design. If you want to go deeper into the knowledge layer, my RAG playlist covers chunking strategies, retrieval quality, evaluation approaches, and observability techniques that directly support production LLM systems. In upcoming videos, I will start building hands-on implementations around LLMOps pipelines, monitoring, and system design patterns. #llmops #genaiarchitecture #productionai #ragarchitecture #mlops #aiengineering #largelanguagemodels #systemdesign #awsai #aiplatform #devopsai #enterpriseai #vectorsearch #aiobservability #promptengineering