Azure Machine Learning: Model Training and Experimentation

Finding the right machine learning model can feel like searching for a needle in a haystack. Azure Machine Learning simplifies this process by providing powerful cloud tools to automate, track, and optimize your model training workflows. In this text-based course, you will transition from a data science enthusiast to a confident practitioner capable of running experiments in the cloud. You will discover how to leverage automated machine learning to quickly identify top-performing models and write clean notebook code to fine-tune your results. What you'll learn: Understand foundational Azure Machine Learning concepts, workspaces, and compute environments; Configure automated machine learning (AutoML) runs to find the best model for your data; Practice writing training scripts and running custom experiments inside cloud notebooks; Apply modern experiment tracking techniques using MLflow integration to log metrics and parameters; Evaluate model performance using key metrics and select the best candidate for deployment. The course begins with core terminology and setting up your workspace environment, then guides you step-by-step through automated workflows, interactive notebook runs, and professional model evaluation practices. This course is designed for aspiring data scientists and developers who are new to Azure Machine Learning, requiring no prior experience with cloud-based AI tools. Start reading today to unlock the power of cloud-scale machine learning experimentation.