MLOps to LLMOps: Managing Large Language Models in Production

As large language models become central to modern software, traditional machine learning workflows must adapt to handle unique challenges like unstructured data, prompt engineering, and semantic search. Moving from classic predictive models to generative AI requires a fundamental shift in how we deploy, monitor, and maintain applications.\n\nThis text-based course guides you through the transition from MLOps to LLMOps, helping you understand what practices remain the same and what new strategies you must adopt. You will gain the foundational knowledge needed to transition your engineering skills to the era of generative AI.\n\nWhat you'll learn:\n- Understand the core differences between traditional machine learning pipelines and large language model workflows.\n- Learn how to manage unstructured data and integrate vector databases for semantic search.\n- Explore retrieval-augmented generation (RAG) patterns and how they change system architecture.\n- Discover evaluation frameworks and monitoring techniques specific to generative outputs and prompt performance.\n- Apply best practices for cost management, rate limiting, and latency optimization in LLM APIs.\n- Master the fundamentals of prompt engineering and version control for semantic prompts.\n\nWe begin by clarifying foundational definitions and comparing classic MLOps with LLMOps. From there, you will read through detailed architectural breakdowns, explore modern data retrieval patterns, and study practical techniques for evaluation and deployment.\n\nThis course is designed for software engineers, data scientists, and technical product managers who are familiar with basic development concepts and want to understand LLM deployment. No advanced machine learning background is required.\n\nStart reading today to bridge the gap between traditional machine learning operations and modern generative AI systems.