Overview

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.

Syllabus

Introduction to Azure Machine Learning Environment

This module lays the groundwork for working with Azure Machine Learning by introducing the course structure and certification scope, guiding learners through the setup of a machine learning workspace, and demonstrating how to manage data through registered data stores and datasets. It provides foundational knowledge necessary to begin experimenting with ML solutions using Azure’s integrated tools.

Compute Infrastructure and Pipelines

This module explores the infrastructure required to build, train, and operationalize machine learning workflows in Azure Machine Learning. Learners will gain hands-on experience setting up compute instances and clusters, constructing visual ML pipelines using Azure ML Designer, integrating custom Python code, and evaluating execution outputs. The module also covers troubleshooting errors and reviewing module results to ensure workflow reliability and model performance.

SDK-Based Development and Automation

This module provides learners with the skills to automate and customize machine learning workflows using the Azure Machine Learning SDK. It introduces the setup of the SDK environment, creating and managing workspaces programmatically, executing model training and experimentation workflows, and implementing AutoML and HyperDrive for advanced automation and tuning. Through hands-on code-driven activities, learners gain experience working with scripts, experiments, pipelines, and hyperparameter optimization.

Model Deployment and Production Pipelines

This module focuses on operationalizing machine learning models by guiding learners through model registration, endpoint deployment, and pipeline publishing using Azure Machine Learning. It covers production-ready compute options, real-time and batch inference deployments, and concludes with best practices for wrapping up a complete ML workflow. By the end of this module, learners will be equipped to transition from experimentation to scalable deployment using both the Designer and SDK approaches.