Azure ML Foundations: Deploying, Managing, and Experimenting with Models

Transitioning from training a model locally to running scalable workflows in the cloud can feel overwhelming. This course simplifies that process by guiding you through the core features of Azure Machine Learning. You will gain the practical skills needed to register datasets, run cloud-based experiments, track performance metrics, and deploy your trained models as accessible web services. What you'll learn: 1. Understand foundational Azure ML concepts, resource groups, and workspace architecture. 2. Configure compute instances and compute clusters for scalable model training. 3. Run and track machine learning experiments using the Azure ML SDK and MLflow. 4. Register, version, and manage trained models in the central model registry. 5. Deploy models as real-time endpoints and test their responsiveness. 6. Apply basic MLOps practices to monitor model performance. The course starts with essential terminology and workspace setup, then progresses through hands-on configuration of compute resources, experiment tracking, and model deployment strategies. This course is designed for beginners in cloud machine learning and data professionals looking to transition their workflows to Azure. No prior cloud engineering experience is required. Start your cloud machine learning journey and streamline your model lifecycle today.