Training Machine Learning Models in Azure Databricks

Scaling machine learning models to handle massive datasets requires the right cloud infrastructure and distributed computing power. Azure Databricks provides a collaborative environment that simplifies big data analytics and machine learning workflows. This text-based course guides you through the foundational concepts of distributed machine learning. You will learn how to prepare large datasets, train models using Spark MLlib, and manage the machine learning lifecycle. What you'll learn: - Understand the core architecture of Azure Databricks and Spark for machine learning. - Prepare and clean large-scale datasets using Spark DataFrames. - Train supervised machine learning models using the Spark MLlib library. - Evaluate model performance using appropriate distributed metrics. - Track experiments and log parameters, metrics, and models with MLflow. - Register and manage model versions for basic MLOps workflows. The course begins with essential terminology and workspace setup, then moves into data preparation, model training, and performance evaluation. Finally, you will explore modern model tracking and lifecycle management tools. This course is designed for aspiring data scientists, data engineers, and developers who are new to distributed machine learning on the cloud. No prior experience with Databricks or Spark is required, though a basic understanding of Python is helpful. Start reading today to build scalable machine learning workflows in the cloud.