Azure Machine Learning: Model Training and Experimentation

Modern machine learning requires more than just writing code; it demands systematic experimentation, tracking, and evaluation to find the best-performing models. This text-based course guides you through the core concepts of cloud-based machine learning workflows using Azure Machine Learning. You will transition from manual, untracked local training to structured cloud experimentation. By reading clear explanations and studying real-world code snippets, you will learn how to automate model selection, track experiments systematically, and ensure your models are fair and explainable. What you will learn: Understand foundational cloud machine learning concepts and environment setup; Configure automated machine learning (AutoML) runs to discover optimal models efficiently; Track training parameters, metrics, and artifacts using MLflow-integrated notebooks; Analyze model performance and fairness with the Responsible AI dashboard; Deploy trained models and understand basic MLOps pipeline concepts. The course begins with essential terminology and workspace setup before moving into practical code walkthroughs for training and tracking. You will progress from basic manual runs to automated workflows and model evaluation. This course is designed for beginner data scientists, software developers, and analysts looking to leverage cloud ML tools. No prior experience with cloud platforms is required, though a basic understanding of Python is helpful. Start your cloud machine learning journey today through structured reading and practical examples.