Training Models with Scripts in Azure Machine Learning

Transitioning your machine learning workflows from local notebooks to the cloud is a critical step for scaling your data science projects. This course provides a clear, step-by-step path to programmatically training models using scripts in the cloud environment. By working through this comprehensive text-based guide, you will transition from manual model training to writing structured, reproducible scripts that run on scalable cloud compute. You will gain a solid foundation in managing cloud assets and automating your training pipelines. What you'll learn: - Understand Azure Machine Learning core architecture, workspaces, and compute resources; - Convert experimental Python code into structured, production-ready training scripts; - Configure and submit training jobs using the modern SDK; - Track parameters, metrics, and outputs automatically using MLflow integration; - Manage environments, dependencies, and data assets in the cloud; - Register and version trained models to establish foundational MLOps practices. We begin with essential cloud ML terminology and workspace setup before diving into script development, environment configuration, and tracking runs. Each concept is reinforced with clear written explanations and practical code structures. This course is designed for aspiring data scientists, developers, and analysts who understand basic Python and want to learn cloud-based model training. No prior cloud experience is required. Start your journey into cloud-based machine learning automation today.