Azure Data Science and Machine Learning for the DP-100 Exam

Machine learning in the cloud is a critical skill for modern data scientists looking to scale their impact and manage complex models. This course provides a clear path to understanding how to leverage Azure Machine Learning services to build robust, production-ready solutions. You will gain the skills to manage the full machine learning lifecycle, from initial data preparation and environment setup to model training, deployment, and performance monitoring. What you'll learn: - Configure workspaces and manage development environments for machine learning - Build and automate training pipelines using the Azure Machine Learning SDK - Train models using automated machine learning and visual designer tools - Deploy models for real-time and batch inference on managed compute targets - Monitor model performance and implement basic MLOps practices for lifecycle management - Understand foundational data science concepts and cloud architecture basics The material starts with core terminology and environment setup before moving into experiment tracking, pipeline automation, and modern deployment strategies. You will progress through written explanations and code-based exercises designed to reinforce practical application. This course is designed for aspiring data scientists and beginners new to cloud-based machine learning; no prior experience with Azure is required to begin. Start building your cloud data science expertise today.