Databricks Machine Learning in Action

Scaling machine learning models from a local environment to enterprise-grade production requires powerful tools and a structured workflow. This text-based course guides you through the core concepts of cloud-scale data science using Databricks, enabling you to manage the entire machine learning lifecycle with confidence. You will transition from understanding fundamental distributed computing concepts to building, tracking, and deploying production-ready models. Through clear written explanations, structured code walkthroughs, and conceptual exercises, you will master the practical workflows used by data scientists to solve real-world problems at scale. What you'll learn: - Understand the foundational architecture of Databricks, PySpark, and distributed machine learning. - Prepare and clean large-scale datasets using modern Spark DataFrame APIs. - Build and train machine learning models using Spark MLlib and integrate popular single-node libraries. - Track experiments, parameters, and metrics using MLflow to ensure reproducibility. - Register, version, and deploy models to production endpoints for real-time serving. - Apply modern MLOps practices, including feature stores and basic pipeline automation. The course begins with essential terminology and setup before moving systematically through data preparation, model training, experiment tracking, and deployment strategies. This course is designed for aspiring data scientists, data engineers, and developers who are new to Databricks and want to build scalable machine learning solutions. No prior experience with distributed computing is required, though a basic familiarity with Python is helpful. Start reading today to master scalable machine learning workflows on Databricks.