LLMOps: Building Production-Ready LLM Systems

LLMOps is the practice of keeping an LLM application reliable under production traffic, within cost limits, and in the face of security threats. In this course, you’ll learn LLMOps by building and operating an application from the ground up with production constraints in mind. You’ll begin with the shift from classical ML to foundation models and the constraints that drove LLMOps: stochastic outputs, high inference costs, and new operational artifacts like prompts and vector indexes. You’ll apply the 4D LLMOps life cycle to define quality gates that prevent the project from stalling at the proof-of-concept stage. You’ll implement a reference RAG architecture, and validate retrieval using golden datasets. Next, you’ll version prompts, enforce structured outputs, and add automated evaluation with LLM-as-a-judge patterns and regression tests. Finally, you’ll prepare for production with security and compliance controls, containerized deployment, and feedback loops to keep quality improving after launch.