Overview

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Learn how to design, develop, automate, and deploy end-to-end machine learning solutions using Microsoft Azure Machine Learning. In this course, you will build practical skills in configuring Azure ML workspaces, managing datasets, creating machine learning pipelines with Azure ML Designer, developing code-driven workflows with the Azure ML SDK, and deploying trained models for real-time and batch inference. Designed for learners who want to build expertise in Azure Machine Learning, this course guides you through the complete machine learning lifecycle—from environment setup and experimentation to automation and production deployment. You will configure compute resources, construct and evaluate pipelines, automate model training with AutoML and HyperDrive, and publish inference pipelines using both the Azure ML Designer and SDK. What makes this course unique is its balanced approach to visual and code-based development, enabling you to understand how Azure ML Designer and the Azure ML SDK work together to support scalable machine learning workflows. Each module builds progressively through scenario-driven lessons that reinforce practical implementation and evaluation of Azure Machine Learning solutions. By the end of the course, you will be able to confidently configure Azure ML environments, develop automated machine learning workflows, optimize experiments, and deploy production-ready machine learning models using Microsoft 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.