Hyperparameter Tuning with Azure Machine Learning

Finding the right hyperparameters for your machine learning models shouldn't be a game of guesswork. Using cloud-scale compute allows you to automate this process efficiently and find the best-performing model configurations. In this course, you will learn how to set up, execute, and analyze hyperparameter tuning jobs using Azure Machine Learning. You will understand how to leverage cloud resources to systematically improve model accuracy and streamline your training workflows. What you'll learn: - Understand the core concepts of hyperparameters, search spaces, and sampling methods. - Configure and run sweep jobs in Azure Machine Learning using modern SDK patterns. - Apply early termination policies to save compute time and control cloud costs. - Analyze training run metrics to identify and select the best-performing model. - Integrate hyperparameter tuning into modern cloud machine learning pipelines. This course begins with foundational definitions of model parameters and tuning strategies before guiding you through step-by-step written explanations and practical code configurations. This course is designed for beginning data scientists and cloud developers who want to scale their machine learning workflows. No prior cloud experience is required, though a basic familiarity with Python is helpful. Start optimizing your machine learning workflows with cloud-scale sweep jobs today.