Azure Machine Learning Foundations for Data Scientists

Building and scaling machine learning models in the cloud is an essential skill for modern data professionals. This course introduces you to cloud-based data science, showing you how to streamline model development without getting bogged down in complex infrastructure. You will transition from local development to the cloud, mastering automated machine learning (AutoML) and visual pipeline tools. By the end of this text-based course, you will understand how to prepare data, train models, and deploy predictive services efficiently. What you'll learn: - Understand core cloud machine learning concepts and cloud workspace architecture. - Configure cloud-based compute resources and secure data storage. - Train predictive models automatically using Automated Machine Learning. - Build end-to-end machine learning pipelines using visual drag-and-drop designer tools. - Deploy trained models as cloud services for real-time predictions. - Apply foundational MLOps principles, including model tracking and responsible AI practices. The course begins with foundational cloud data concepts and workspace setup before moving into automated model training, visual pipeline design, and deployment strategies. You will progress through clear written explanations and practical step-by-step walkthroughs. This course is designed for aspiring data scientists, analysts, and developers who want to leverage cloud power for machine learning. No prior cloud experience is required. Start your cloud data science journey today and learn to build scalable predictive models.