MLOps and MLflow for Deploying Multiple LLMs

Managing and serving Large Language Models requires robust infrastructure and dependable lifecycle workflows. This text-based course guides you through foundational MLOps concepts and MLflow tools to deploy, track, and interface with multiple LLMs in production environments. You will gain the practical skills needed to organize model runs, register artifacts, set up deployment servers, and connect model endpoints to interactive user interfaces. What you'll learn: - Understand core MLOps principles, model lifecycle phases, and MLflow architecture - Track experiments, parameters, and evaluation metrics across various model versions - Configure the MLflow Model Registry to stage and manage multiple language models - Deploy model serving endpoints capable of handling requests across different LLMs - Connect API endpoints to frontend interfaces for direct model interaction - Apply modern LLMOps practices for prompt tracking, routing, and basic model observability The curriculum starts with essential definitions and core MLOps terminology before guiding you step-by-step through registry setup, serving configurations, and multi-model application architecture. This course is designed for beginners and developers with basic programming knowledge looking to enter the world of model deployment without complicated prerequisites. Start reading today to establish your foundation in modern LLM operations.