Learn MLOps with MLflow and Databricks – Full Course for Machine Learning Engineers

A comprehensive end-to-end course on MLflow covering experiment tracking, parameter and metric logging, model versioning, model registry, deployment via HTTP endpoints, and LLMOps for managing prompts. The course walks through setting up MLflow locally, understanding backend and artifact stores, creating experiments and runs, logging various artifact types, registering models, deploying models as REST APIs, and batch inferencing. It also introduces how GenAI/LLM workflows differ from traditional MLOps and how MLflow supports prompt management.