Databricks MLOps and LLMOps Fundamentals

Modern data teams require structured workflows to take machine learning models and large language models from development into production. This text-based course guides you step by step through the essentials of MLOps and LLMOps using Databricks and MLflow, giving you the practical knowledge to manage model lifecycles effectively. You will begin with fundamental definitions and core terminology, then progress toward configuring experiments, tracking model metrics, versioning artifacts, and managing transformer-based workflows. What you'll learn: - Understand core MLOps and LLMOps terminology, architecture, and lifecycle stages - Configure Databricks workspaces and clusters for machine learning development - Track parameters, metrics, and models using MLflow Tracking and the MLflow Model Registry - Manage transformer models and fine-tuning workflows with modern operational standards - Apply basic model serving and monitoring concepts for production reliability - Implement vector storage and retrieval-augmented patterns within Databricks The course starts with essential vocabulary and foundational concepts before advancing into practical code walkthroughs and deployment patterns. Designed specifically for beginners, this course requires no prior MLOps experience, though basic familiarity with Python is helpful. Start reading today to build a strong foundation in modern machine learning operations.