DP-100: Microsoft Azure ML Solutions

This comprehensive course enables learners to design, implement, and deploy end-to-end machine learning solutions using Microsoft Azure Machine Learning. Through hands-on guidance, learners will configure development environments, build interactive experiments using Azure ML Designer, develop automation workflows via the SDK, and deploy models for real-time and batch inference using production-ready compute targets. The course is structured into four skill-building modules that introduce foundational cloud ML concepts, construct pipelines and SDK-based experiments, apply automation tools such as AutoML and HyperDrive, and publish trained models to production environments. Each module reinforces concepts through scenario-driven lessons that use Bloom’s Taxonomy to identify, configure, implement, analyze, and evaluate Azure ML workflows. By the end of this course, learners will be equipped to transition from experimentation to scalable deployment with full lifecycle awareness in Azure Machine Learning

• Configure Azure ML development environments
• Build interactive experiments using Azure ML Designer
• Develop automation workflows via SDK
• Deploy models for real-time and batch inference
• Apply AutoML and HyperDrive for model optimization
• Engage with scenario-based lessons for practical learning

• Familiarity with Python, basic machine learning concepts, and cloud computing fundamentals recommended