Designing Machine Learning Solutions in Azure

Cloud-based machine learning is transforming how businesses solve complex problems, but navigating cloud ML tools can feel overwhelming. This text-based course simplifies the journey by guiding you through the core concepts of cloud-scale machine learning. You will transition from understanding basic data science concepts to designing and structuring end-to-end machine learning workflows in the cloud. You will learn how to prepare data, train models, and manage assets efficiently using modern cloud tools and SDKs. What you'll learn: Understand foundational machine learning concepts and how they map to cloud resources; Configure workspace environments, compute targets, and data stores; Design robust training pipelines using the modern Azure Machine Learning SDK; Track experiments and log metrics using MLflow integration for better model reproducibility; Deploy trained models to managed endpoints for real-time inference; Explore foundation models and modern generative AI capabilities within the cloud platform. The course begins with essential terminology and workspace setup before moving into step-by-step written walkthroughs of model training, tracking, and deployment strategies. Designed for beginners to cloud machine learning, data enthusiasts, and aspiring cloud engineers, this course requires no prior cloud experience. Start reading today to build your foundation in cloud-based machine learning.