Azure Machine Learning Step 4: Modeling & Experimentation

In this fourth session of the Azure Machine Learning series, we’ll build on the foundation of clean, well-prepared data and move into the core of machine learning: training models and running experiments. Azure Machine Learning provides powerful capabilities for building, tracking, and optimizing models at scale, enabling teams to move from data to insights efficiently and reproducibly.

This session focuses on the practical side of modeling and experimentation within Azure ML. You’ll learn how to take prepared datasets and turn them into trained models while leveraging Azure ML’s experiment tracking, automation, and scalability features. Whether you’re continuing from Step 3 or already have experience with ML concepts, this session will help you operationalize your modeling workflow in Azure.

You’ll learn:

• How modeling fits into the machine learning lifecycle
• How to create and run training jobs in Azure ML (Studio, SDK, CLI)
• How to structure experiments and track runs using MLflow
• Techniques for training models using frameworks like scikit-learn, PyTorch, and TensorFlow
• How to manage hyperparameters and perform tuning (including AutoML)
• How to evaluate model performance using key metrics and visualizations
• Best practices for experiment organization, reproducibility, and collaboration
• How trained models are registered and prepared for deployment

This session is designed to help you move from “I have prepared data” to “I can train, evaluate, and track models effectively in Azure ML.” If you’re ready to bring your data to life and start building real machine learning models, this is your next step.