Machine Learning Automation with Azure Databricks AutoML

Setting up machine learning pipelines from scratch can be time-consuming and complex. Azure Databricks AutoML simplifies this process by automating model selection, hyperparameter tuning, and feature engineering. In this text-based course, you will learn how to harness automated machine learning to generate high-quality models directly within your Databricks workspace. You will transition from manual experimentation to automated, trackable workflows that accelerate your data science projects. What you'll learn: - Understand the foundational concepts of automated machine learning and the Databricks architecture. - Configure and run AutoML experiments for classification, regression, and forecasting tasks. - Evaluate model performance using key metrics and select the best candidate for your data. - Track experiments and manage model versions using integrated MLflow capabilities. - Deploy trained models to production endpoints for real-time or batch scoring. The course begins with core definitions and workspace setup before guiding you through data preparation, experiment execution, and model evaluation. You will wrap up by exploring essential MLOps practices like model registration and deployment. This course is designed for aspiring data scientists, analysts, and developers who want to start using machine learning automation without deep prior programming experience. Start reading today to streamline your machine learning development workflow.