MLOps with MLflow and Databricks Fundamentals

Managing machine learning projects requires reliable systems to track experiments, manage model versions, and automate deployment workflows. This text-based course guides you through the core principles of MLOps using industry-standard tools like MLflow and Databricks. You will build a solid understanding of how to transition machine learning workflows from basic scripts into organized, reproducible pipelines. Through clear written explanations and practical code examples, you will learn how to log metrics, compare runs, manage artifacts, and optimize model performance systematically. What you'll learn: - Understand core MLOps terminology, lifecycle stages, and architectural patterns - Configure and navigate MLflow tracking servers and Databricks workspaces - Log parameters, metrics, and model artifacts across iterative experiments - Apply automated hyperparameter tuning workflows to optimize model outcomes - Register, version, and manage models inside the MLflow Model Registry - Implement reproducible project packaging and deployment best practices The course begins with fundamental concepts of machine learning operations before moving through hands-on experiment tracking, model registry workflows, and tuning strategies. It is designed for beginners in MLOps, data science enthusiasts, and developers with basic Python knowledge who want to build structured machine learning systems. Start reading today and bring structure to your machine learning projects.