Azure Machine Learning: Building and Managing Solutions

Scaling machine learning from a local notebook to a robust cloud environment requires a specialized set of tools and automated workflows. This course provides a foundational guide to using Azure Machine Learning to manage the entire lifecycle of a data science project. You will learn to navigate the cloud ecosystem, from initial data ingestion to the final deployment of predictive services. You will gain the skills needed to move models from experimental code to reliable production assets while maintaining full control over the development process. This curriculum also serves as a key resource for those preparing for professional data science certification. What you'll learn: - Understand the core architecture of cloud-based machine learning workspaces - Configure scalable compute resources and secure data storage for model training - Develop machine learning experiments and automated pipelines using the Python SDK - Deploy trained models as production-ready inference endpoints - Apply MLOps principles for versioning, tracking, and reproducibility - Monitor model health and detect data drift in live environments The content begins with fundamental definitions and workspace setup, progressing through the practical steps of training, registering, and operationalizing models in an enterprise context. You will learn through clear written explanations and code-based examples designed for real-world application. This course is designed for beginners entering the field of cloud data science; while basic Python knowledge is helpful, no prior experience with cloud platforms is required. Begin your journey into cloud-based machine learning operations today.