Machine Learning with Azure Databricks: Build and Deploy ML Models

Organizations today require scalable platforms to handle massive datasets and train complex machine learning models efficiently. Azure Databricks provides a unified, collaborative environment that bridges the gap between data engineering and data science. This text-based course guides you from the absolute basics of cloud data science to building and managing your own machine learning pipelines. You will gain a solid understanding of how to configure workspaces, process data at scale, and track your experiments systematically. What you'll learn: Understand foundational machine learning concepts and Azure Databricks workspace architecture; Prepare and transform large-scale datasets using Spark dataframes; Train and tune machine learning models using popular open-source libraries; Track experiments, metrics, and parameters using integrated MLflow tracking; Register, version, and manage models for deployment readiness; Apply modern workflows to automate and scale machine learning pipelines. The course starts with essential cloud and data science terminology before moving into practical code walkthroughs. You will read through step-by-step explanations, analyze real-world code snippets, and complete written exercises designed to reinforce your learning. This course is designed for aspiring data scientists, data engineers, and developers who are new to Azure Databricks. No prior experience with cloud machine learning is required, though a basic familiarity with Python is helpful. Start building scalable machine learning solutions on the cloud today.